blood plasma lipidomics - Data analysis (+)

Load libraries

library(readxl) # for reading in excel files
library(janitor) # data checks and cleaning
library(glue) # for easy pasting
library(FactoMineR) # for PCA
library(factoextra) # for PCA
library(rstatix) # for stats
library(pheatmap) # for heatmaps
library(plotly) # for interactive plots
library(htmlwidgets) # for saving interactive plots
library(devtools)
library(notame) # used for feature clustering
library(doParallel)
library(igraph) # feature clustering
library(ggpubr) # visualizations
library(knitr) # clean table printing
library(rmarkdown)
library(corrr)
library(ggcorrplot)
library(ggthemes)
library(ggtext)
library(PCAtools)
library(pathview) # for functional analysis and KEGG annotation
library(mixOmics) #multilevel PCA and sPLS-DA
library(lipidr)
library(patchwork)
library(tidyverse) # for everything

Read in data

# raw filtered metabolomics data 
omicsdata <- read_csv("features_post-data-filtering.csv") %>%
  clean_names()

# metadata
metadata <- read_csv("../../../metadata-new.csv")

# carotenoids data
carotenoids <- read_excel("../../../Plasma carot_ret_apo levels.xlsx",
                          sheet = "B1andB3") %>%
  clean_names()

Wrangle data

Metadata

carotenoids <- carotenoids %>%
  mutate(total_carotenoids = b_c_nmol_l_plasma + lyc_nmol_l_plasma + retinol_nmol_l_plasma + apo13one_nmol_l_plasma)

Feature data

# how many features
nrow(omicsdata)

## [1] 2450

# are there any duplicates?
omicsdata %>% get_dupes(mz_rt)

## # A tibble: 12 × 89
##    mz_rt         dupe_count row_id x5109_b1_beta_c18pos…¹ x5101_b1_control_c18…²
##    <chr>              <int>  <dbl>                  <dbl>                  <dbl>
##  1 368.2792_0.6…          2   1042                 10114.                 24509.
##  2 368.2792_0.6…          2   1057                 10114.                 24509.
##  3 717.5903_6.8…          2   4952                 31805.                 49139.
##  4 717.5903_6.8…          2   4956                 31805.                 49139.
##  5 720.5531_6.8…          2   5040                 20646                  23011.
##  6 720.5531_6.8…          2   5070                 20646                  23011.
##  7 812.6663_7.96          2   7214                 20484.                 24697.
##  8 812.6663_7.96          2   7301                 20104.                 24697.
##  9 814.5717_7.3…          2   6253                 10382.                 13812.
## 10 814.5717_7.3…          2   6257                 10382.                 13812.
## 11 931.5277_8.5…          2   8080                 18478.                 15492.
## 12 931.5277_8.5…          2   8092                 18478.                 15492.
## # ℹ abbreviated names: ¹​x5109_b1_beta_c18pos_65, ²​x5101_b1_control_c18pos_82
## # ℹ 84 more variables: x5109_b3_beta_c18pos_81 <dbl>,
## #   x5103_b3_lyc_c18pos_63 <dbl>, x5108_b1_control_c18pos_71 <dbl>,
## #   x5113_b1_beta_c18pos_53 <dbl>, x5112_b3_beta_c18pos_56 <dbl>,
## #   x5105_b1_lyc_c18pos_76 <dbl>, x5110_b3_lyc_c18pos_84 <dbl>,
## #   x5110_b1_lyc_c18pos_59 <dbl>, x5101_b3_control_c18pos_66 <dbl>,
## #   x5113_b3_beta_c18pos_47 <dbl>, x5103_b1_lyc_c18pos_35 <dbl>, …

# remove dupes
omicsdata <- omicsdata %>% 
  distinct(mz_rt, .keep_all = TRUE)

# check again for dupes
omicsdata %>% get_dupes(mz_rt)

## # A tibble: 0 × 89
## # ℹ 89 variables: mz_rt <chr>, dupe_count <int>, row_id <dbl>,
## #   x5109_b1_beta_c18pos_65 <dbl>, x5101_b1_control_c18pos_82 <dbl>,
## #   x5109_b3_beta_c18pos_81 <dbl>, x5103_b3_lyc_c18pos_63 <dbl>,
## #   x5108_b1_control_c18pos_71 <dbl>, x5113_b1_beta_c18pos_53 <dbl>,
## #   x5112_b3_beta_c18pos_56 <dbl>, x5105_b1_lyc_c18pos_76 <dbl>,
## #   x5110_b3_lyc_c18pos_84 <dbl>, x5110_b1_lyc_c18pos_59 <dbl>,
## #   x5101_b3_control_c18pos_66 <dbl>, x5113_b3_beta_c18pos_47 <dbl>, …

# how many features
nrow(omicsdata)

## [1] 2444

Sometimes a weird logical column (lgl) comes up in my data. Let’s check if it’s there

colnames(omicsdata)

##  [1] "mz_rt"                      "row_id"                    
##  [3] "x5109_b1_beta_c18pos_65"    "x5101_b1_control_c18pos_82"
##  [5] "x5109_b3_beta_c18pos_81"    "x5103_b3_lyc_c18pos_63"    
##  [7] "x5108_b1_control_c18pos_71" "x5113_b1_beta_c18pos_53"   
##  [9] "x5112_b3_beta_c18pos_56"    "x5105_b1_lyc_c18pos_76"    
## [11] "x5110_b3_lyc_c18pos_84"     "x5110_b1_lyc_c18pos_59"    
## [13] "x5101_b3_control_c18pos_66" "x5113_b3_beta_c18pos_47"   
## [15] "x5103_b1_lyc_c18pos_35"     "x5104_b3_lyc_c18pos_32"    
## [17] "x5112_b1_beta_c18pos_34"    "x5107_b3_beta_c18pos_28"   
## [19] "x5107_b1_beta_c18pos_29"    "x5102_b3_control_c18pos_33"
## [21] "x5111_b3_control_c18pos_39" "x5105_b3_lyc_c18pos_21"    
## [23] "x5108_b3_control_c18pos_22" "x5102_b1_control_c18pos_5" 
## [25] "x5111_b1_control_c18pos_4"  "x5104_b1_lyc_c18pos_10"    
## [27] "x5115_b3_lyc_c18pos_70"     "x5115_b1_lyc_c18pos_36"    
## [29] "x5125_b1_control_c18pos_80" "x5118_b1_lyc_c18pos_57"    
## [31] "x5121_b1_beta_c18pos_69"    "x5119_b1_control_c18pos_74"
## [33] "x5122_b3_lyc_c18pos_75"     "x5117_b1_lyc_c18pos_46"    
## [35] "x5114_b3_beta_c18pos_23"    "x5124_b3_control_c18pos_68"
## [37] "x5125_b3_control_c18pos_51" "x5114_b1_beta_c18pos_26"   
## [39] "x5120_b1_beta_c18pos_72"    "x5119_b3_control_c18pos_58"
## [41] "x5121_b3_beta_c18pos_48"    "x5116_b3_control_c18pos_20"
## [43] "x5126_b1_beta_c18pos_62"    "x5123_b3_control_c18pos_40"
## [45] "x5116_b1_control_c18pos_6"  "x5124_b1_control_c18pos_45"
## [47] "x5117_b3_lyc_c18pos_2"      "x5122_b1_lyc_c18pos_14"    
## [49] "x5118_b3_lyc_c18pos_9"      "x5123_b1_control_c18pos_11"
## [51] "x5120_b3_beta_c18pos_54"    "x5127_b3_control_c18pos_83"
## [53] "x5129_b1_lyc_c18pos_78"     "x5128_b3_lyc_c18pos_60"    
## [55] "x5134_b3_beta_c18pos_64"    "x5136_b3_beta_c18pos_77"   
## [57] "x5127_b1_control_c18pos_30" "x5132_b3_beta_c18pos_52"   
## [59] "x5130_b3_lyc_c18pos_50"     "x5131_b1_beta_c18pos_38"   
## [61] "x5126_b3_beta_c18pos_24"    "x5129_b3_lyc_c18pos_16"    
## [63] "x5134_b1_beta_c18pos_42"    "x5132_b1_beta_c18pos_41"   
## [65] "x5133_b1_control_c18pos_27" "x5136_b1_beta_c18pos_44"   
## [67] "x5130_b1_lyc_c18pos_17"     "x5128_b1_lyc_c18pos_8"     
## [69] "x5135_b3_lyc_c18pos_18"     "x5133_b3_control_c18pos_15"
## [71] "x5131_b3_beta_c18pos_3"     "x5135_b1_lyc_c18pos_12"    
## [73] "qc3_c18pos_13"              "qc2_c18pos_7"              
## [75] "qc11_c18pos_61"             "qc14_c18pos_79"            
## [77] "qc9_c18pos_49"              "qc10_c18pos_55"            
## [79] "qc6_c18pos_31"              "qc8_c18pos_43"             
## [81] "qc5_c18pos_25"              "qc15_c18pos_85"            
## [83] "qc13_c18pos_73"             "qc7_c18pos_37"             
## [85] "qc12_c18pos_67"             "qc4_c18pos_19"             
## [87] "qc1_c18pos_1"               "x88"

# remove lgl column
omicsdata <- omicsdata %>%
  dplyr::select(!where(is.logical)) 

colnames(omicsdata)

##  [1] "mz_rt"                      "row_id"                    
##  [3] "x5109_b1_beta_c18pos_65"    "x5101_b1_control_c18pos_82"
##  [5] "x5109_b3_beta_c18pos_81"    "x5103_b3_lyc_c18pos_63"    
##  [7] "x5108_b1_control_c18pos_71" "x5113_b1_beta_c18pos_53"   
##  [9] "x5112_b3_beta_c18pos_56"    "x5105_b1_lyc_c18pos_76"    
## [11] "x5110_b3_lyc_c18pos_84"     "x5110_b1_lyc_c18pos_59"    
## [13] "x5101_b3_control_c18pos_66" "x5113_b3_beta_c18pos_47"   
## [15] "x5103_b1_lyc_c18pos_35"     "x5104_b3_lyc_c18pos_32"    
## [17] "x5112_b1_beta_c18pos_34"    "x5107_b3_beta_c18pos_28"   
## [19] "x5107_b1_beta_c18pos_29"    "x5102_b3_control_c18pos_33"
## [21] "x5111_b3_control_c18pos_39" "x5105_b3_lyc_c18pos_21"    
## [23] "x5108_b3_control_c18pos_22" "x5102_b1_control_c18pos_5" 
## [25] "x5111_b1_control_c18pos_4"  "x5104_b1_lyc_c18pos_10"    
## [27] "x5115_b3_lyc_c18pos_70"     "x5115_b1_lyc_c18pos_36"    
## [29] "x5125_b1_control_c18pos_80" "x5118_b1_lyc_c18pos_57"    
## [31] "x5121_b1_beta_c18pos_69"    "x5119_b1_control_c18pos_74"
## [33] "x5122_b3_lyc_c18pos_75"     "x5117_b1_lyc_c18pos_46"    
## [35] "x5114_b3_beta_c18pos_23"    "x5124_b3_control_c18pos_68"
## [37] "x5125_b3_control_c18pos_51" "x5114_b1_beta_c18pos_26"   
## [39] "x5120_b1_beta_c18pos_72"    "x5119_b3_control_c18pos_58"
## [41] "x5121_b3_beta_c18pos_48"    "x5116_b3_control_c18pos_20"
## [43] "x5126_b1_beta_c18pos_62"    "x5123_b3_control_c18pos_40"
## [45] "x5116_b1_control_c18pos_6"  "x5124_b1_control_c18pos_45"
## [47] "x5117_b3_lyc_c18pos_2"      "x5122_b1_lyc_c18pos_14"    
## [49] "x5118_b3_lyc_c18pos_9"      "x5123_b1_control_c18pos_11"
## [51] "x5120_b3_beta_c18pos_54"    "x5127_b3_control_c18pos_83"
## [53] "x5129_b1_lyc_c18pos_78"     "x5128_b3_lyc_c18pos_60"    
## [55] "x5134_b3_beta_c18pos_64"    "x5136_b3_beta_c18pos_77"   
## [57] "x5127_b1_control_c18pos_30" "x5132_b3_beta_c18pos_52"   
## [59] "x5130_b3_lyc_c18pos_50"     "x5131_b1_beta_c18pos_38"   
## [61] "x5126_b3_beta_c18pos_24"    "x5129_b3_lyc_c18pos_16"    
## [63] "x5134_b1_beta_c18pos_42"    "x5132_b1_beta_c18pos_41"   
## [65] "x5133_b1_control_c18pos_27" "x5136_b1_beta_c18pos_44"   
## [67] "x5130_b1_lyc_c18pos_17"     "x5128_b1_lyc_c18pos_8"     
## [69] "x5135_b3_lyc_c18pos_18"     "x5133_b3_control_c18pos_15"
## [71] "x5131_b3_beta_c18pos_3"     "x5135_b1_lyc_c18pos_12"    
## [73] "qc3_c18pos_13"              "qc2_c18pos_7"              
## [75] "qc11_c18pos_61"             "qc14_c18pos_79"            
## [77] "qc9_c18pos_49"              "qc10_c18pos_55"            
## [79] "qc6_c18pos_31"              "qc8_c18pos_43"             
## [81] "qc5_c18pos_25"              "qc15_c18pos_85"            
## [83] "qc13_c18pos_73"             "qc7_c18pos_37"             
## [85] "qc12_c18pos_67"             "qc4_c18pos_19"             
## [87] "qc1_c18pos_1"

Tidy

# create long df for omics df
omicsdata_tidy <- omicsdata %>%
  pivot_longer(cols = 3:ncol(.),
               names_to = "sample",
               values_to = "peak_height") %>%
  mutate(sample2 = sample) %>%
  # add a new column with just subject
  dplyr::rename("subject" = sample2) %>%
  # remove the suffix from subject names
  mutate_at("subject", str_sub, start=2, end=5)

Combine dfs

## tibble [207,740 × 18] (S3: tbl_df/tbl/data.frame)
##  $ subject          : chr [1:207740] "5109" "5101" "5109" "5103" ...
##  $ sample           : chr [1:207740] "x5109_b1_beta_c18pos_65" "x5101_b1_control_c18pos_82" "x5109_b3_beta_c18pos_81" "x5103_b3_lyc_c18pos_63" ...
##  $ treatment        : chr [1:207740] "beta" "control" "beta" "red" ...
##  $ tomato_or_control: chr [1:207740] "tomato" "control" "tomato" "tomato" ...
##  $ mz               : num [1:207740] 189 189 189 189 189 ...
##  $ rt               : num [1:207740] 0.513 0.513 0.513 0.513 0.513 0.513 0.513 0.513 0.513 0.513 ...
##  $ row_id           : num [1:207740] 204 204 204 204 204 204 204 204 204 204 ...
##  $ peak_height      : num [1:207740] 5394 3299 6079 4923 3050 ...
##  $ sex              : chr [1:207740] "M" "F" "M" "M" ...
##  $ bmi              : num [1:207740] 26.1 29.4 26.1 29.7 22.7 ...
##  $ age              : num [1:207740] 65 55 65 50 52 34 60 61 58 58 ...
##  $ tot_chol         : num [1:207740] 127 235 127 172 177 141 235 189 176 176 ...
##  $ ldl_chol         : num [1:207740] 69.4 150.4 69.4 111.4 120 ...
##  $ hdl_chol         : num [1:207740] 40 71 40 53 44 44 52 45 66 66 ...
##  $ triglycerides    : num [1:207740] 88 68 88 38 65 55 169 92 47 47 ...
##  $ glucose          : num [1:207740] 87 90 87 92 91 99 92 103 92 92 ...
##  $ SBP              : num [1:207740] 147 156 147 106 139 104 107 131 109 109 ...
##  $ DBP              : num [1:207740] 76 90 76 71 81 71 70 78 70 70 ...

# replace NA's in certain columns with QC
meta_omics_sep$subject <- str_replace_all(meta_omics_sep$subject, "c", "qc")
  

meta_omics_sep$sample <- meta_omics_sep$sample %>%
  replace_na("QC")

meta_omics_sep$treatment <- meta_omics_sep$treatment %>%
  replace_na("QC")

meta_omics_sep$tomato_or_control <- meta_omics_sep$tomato_or_control %>%
  replace_na("QC")

Data summaries

Number of masses detected

nrow(omicsdata)

## [1] 2444

Mass range for metabolites detected?

range(meta_omics_sep$mz)

## [1]  100.0757 1694.3938

RT range for metabolites detected?

range(meta_omics_sep$rt)

## [1]  0.513 12.336

Mass vs RT scatterplot

# plot
(plot_mzvsrt <- meta_omics_sep %>%
  ggplot(aes(x = rt, y = mz)) +
  geom_point() +
  theme_minimal() +
  labs(x = "Retention time, min",
       y = "m/z",
       title = "mz across RT for all features"))

Histogram for mass range

meta_omics_sep %>%
  group_by(mz) %>%
  ggplot(aes(x = mz)) +
  geom_histogram(binwidth = 25) +
  theme_minimal() +
  labs(x = "Monoisotopic mass (amu)",
       y = "Number of features",
       title = "Distribution of features by mass")

Histogram for RT

meta_omics_sep %>%
  group_by(mz) %>%
  ggplot(aes(x = rt)) +
  geom_histogram(binwidth = 0.1) + # 6 second bins
  theme_minimal() +
  labs(x = "Retention time",
       y = "Number of features",
       title = "Distribution of features by retention time")

NAs and imputing

NAs

# samples only (no QCs)
omicsdata_noQC <- omicsdata %>%
  dplyr::select(-contains("qc"))

#NAs in samples only?
NAbyRow_noQC <- rowSums(is.na(omicsdata_noQC[,-1]))

hist(NAbyRow_noQC,
     breaks = 70, # because there are 70 samples 
     xlab = "Number of missing values",
     ylab = "Number of metabolites",
     main = "How many missing values are there?")

Are there any missing values in QCs? There shouldn’t be after data preprocessing/filtering

omicsdata_QC <- omicsdata %>%
  dplyr::select(starts_with("qc")) 

NAbyRow_QC <- colSums(is.na(omicsdata_QC))
# lets confirm that there are no missing values from my QCs
sum(NAbyRow_QC) # no

## [1] 0

# calculate how many NAs there are per feature in whole data set
contains_NAs <- meta_omics %>%
  group_by(mz_rt) %>%
  dplyr::count(is.na(peak_height)) %>%
  filter(`is.na(peak_height)` == TRUE)
kable(contains_NAs)

mz_rt	is.na(peak_height)	n
1050.6573_2.737	TRUE	1
1051.1585_2.738	TRUE	1
1052.864_11.17	TRUE	2
1064.8634_11.211	TRUE	21
1077.6264_2.732	TRUE	2
1192.8093_6.322	TRUE	1
1213.4964_3.94	TRUE	3
1260.0333_9.997	TRUE	15
1272.1559_12.098	TRUE	1
1312.1372_10.911	TRUE	14
1321.82_2.739	TRUE	16
1332.9345_6.33	TRUE	1
1339.8437_3.087	TRUE	32
1378.109_6.06	TRUE	2
1413.0725_6.543	TRUE	10
1416.1569_11.643	TRUE	4
1417.1607_11.643	TRUE	4
1419.0884_6.065	TRUE	1
1424.1384_8.941	TRUE	2
1443.1241_6.536	TRUE	22
1460.0658_6.066	TRUE	6
1486.0818_6.746	TRUE	10
1488.1014_8.407	TRUE	3
149.0596_1.28	TRUE	66
1496.0473_7.572	TRUE	8
1512.0991_6.376	TRUE	1
152.0706_0.642	TRUE	56
1558.081_6.341	TRUE	1
1572.0922_6.339	TRUE	13
1580.9848_2.737	TRUE	7
1582.0806_6.324	TRUE	1
1583.0843_6.325	TRUE	1
1596.6247_6.715	TRUE	1
1602.3814_11.358	TRUE	36
1603.3857_11.359	TRUE	33
162.0914_0.651	TRUE	67
1628.3971_11.365	TRUE	15
1628.4765_11.515	TRUE	23
1629.4012_11.367	TRUE	8
1633.4321_11.51	TRUE	16
1636.2128_8.395	TRUE	2
1651.3848_11.251	TRUE	9
1653.489_11.522	TRUE	4
1654.4923_11.523	TRUE	4
1655.4167_11.377	TRUE	1
1657.5142_11.686	TRUE	20
1664.1589_6.841	TRUE	5
167.0854_1.05	TRUE	61
1676.3968_11.262	TRUE	2
1677.4008_11.264	TRUE	2
1678.4082_11.291	TRUE	3
172.1333_0.62	TRUE	46
201.0466_0.675	TRUE	64
203.0439_0.671	TRUE	65
207.1109_0.641	TRUE	1
219.1127_0.668	TRUE	66
229.0945_0.651	TRUE	67
230.073_0.636	TRUE	68
240.1585_0.692	TRUE	59
254.1387_0.695	TRUE	54
256.1695_0.637	TRUE	60
259.0944_0.697	TRUE	62
268.1541_0.91	TRUE	66
272.1651_0.645	TRUE	56
275.068_0.694	TRUE	66
275.1306_0.638	TRUE	66
277.1263_0.644	TRUE	3
308.1256_1.117	TRUE	3
308.1855_0.633	TRUE	20
312.1597_1.413	TRUE	1
315.1224_1.279	TRUE	48
320.2375_0.643	TRUE	69
337.1048_1.268	TRUE	6
346.1226_0.659	TRUE	68
353.079_1.243	TRUE	62
365.1356_1.988	TRUE	1
383.1533_0.931	TRUE	43
389.1625_0.672	TRUE	66
391.1601_0.671	TRUE	65
398.3413_0.639	TRUE	40
399.2641_1.416	TRUE	1
413.2797_1.415	TRUE	7
415.1686_0.643	TRUE	63
423.1686_0.697	TRUE	59
425.1649_0.679	TRUE	29
426.0969_1.177	TRUE	68
426.3213_1.384	TRUE	20
429.24_0.73	TRUE	3
430.2589_1.992	TRUE	11
432.202_0.638	TRUE	64
439.1646_0.682	TRUE	64
463.2323_1.708	TRUE	1
466.3162_1.004	TRUE	19
484.2854_0.667	TRUE	65
531.2196_1.712	TRUE	2
533.3253_0.69	TRUE	39
536.2833_2.749	TRUE	38
616.1763_0.902	TRUE	2
634.4366_0.676	TRUE	1
643.38_0.69	TRUE	6
664.5997_4.075	TRUE	41
682.5424_8.581	TRUE	6
682.5516_8.574	TRUE	4
706.612_10.9	TRUE	6
712.5953_4.577	TRUE	9
736.5043_0.684	TRUE	1
748.5833_7.03	TRUE	2
750.599_7.485	TRUE	3
764.6932_10.985	TRUE	2
766.5377_7.38	TRUE	1
766.6703_10.657	TRUE	13
785.5418_7.137	TRUE	4
786.6508_7.289	TRUE	1
794.7051_10.958	TRUE	5
798.564_5.251	TRUE	11
798.5655_5.067	TRUE	13
798.5661_4.516	TRUE	14
800.5792_5.005	TRUE	25
802.5954_5.397	TRUE	21
807.3448_6.714	TRUE	1
822.5634_4.827	TRUE	41
822.563_5.243	TRUE	36
822.6001_6.635	TRUE	2
822.6568_10.855	TRUE	1
826.5945_5.722	TRUE	44
826.594_5.988	TRUE	33
826.6326_7.901	TRUE	1
844.5121_3.103	TRUE	3
854.4963_2.738	TRUE	10
872.7324_11.418	TRUE	5
894.5999_5.902	TRUE	27
973.7251_11.18	TRUE	2
990.7559_11.04	TRUE	12
992.7701_11.13	TRUE	1

Remove NAs

There are some features that are missing in a majority of subjects. I’m going to remove those because they may skew the data.

# number of features
nrow(omicsdata)

## [1] 2444

# Removing features missing from over 90% of data
omit_features <- contains_NAs %>%
  filter(n/70 >= 0.90)

#preview
nrow(omit_features) # features to remove

## [1] 19

# how many features should be left?
nrow(omicsdata) - nrow(omit_features)

## [1] 2425

# now remove these features from the omics dataset
omicsdata <- omicsdata %>%
  anti_join(omit_features,
            by = "mz_rt")

 # check number of features now?
nrow(omicsdata)

## [1] 2425

Data imputation

# impute any missing values by replacing them with 1/2 of the lowest peak height value of a feature (i.e. in a row).
imputed_omicsdata <- omicsdata

imputed_omicsdata[] <- lapply(imputed_omicsdata, 
                              function(x) ifelse(is.na(x),
                                                 min(x, na.rm = TRUE)/2, x))

dim(imputed_omicsdata)

## [1] 2425   87

Are there any NAs?

imputed_omicsdata %>%
  is.na() %>%
  sum()

## [1] 0

# imputations worked

New tidy df

# create long df for imputed omics df
imputed_omicsdata_tidy <- imputed_omicsdata %>%
  pivot_longer(cols = 3:ncol(.),
               names_to = "sample",
               values_to = "peak_height") %>%
  mutate(sample2 = sample) %>%
  # add a new column with just subject
  dplyr::rename("subject" = sample2) %>%
  # remove the suffix from subject names
  mutate_at("subject", str_sub, start=2, end=5) 

# combine meta and imputed omics dfs
imp_meta_omics <- full_join(imputed_omicsdata_tidy,
                         metadata,
                         by = c("subject" = "subject"))

# separate mz and rt
imp_meta_omics_sep <- imp_meta_omics %>%
  separate(col = mz_rt,
           into = c("mz", "rt"),
           sep = "_") 

# convert columns to correct type
imp_meta_omics_sep$mz <- as.numeric(imp_meta_omics_sep$mz)
imp_meta_omics_sep$rt <- as.numeric(imp_meta_omics_sep$rt)

Notame feature reduction

vignette for reference

#browseVignettes("notame")

Let’s look at what masses come up at each RT again

# rt vs mz plot
imp_meta_omics_sep %>%
  ggplot(aes(x = rt, y = mz)) +
  geom_point() +
  theme_minimal() +
  labs(x = "RT (min)",
       y = "mz")

There are some points that are at the same RT, meaning they could be coming from the same compound. We’ll run notame clustering to collapse features coming from one mass into one feature.

Data restructuring for notame

# create features list from imputed data set to only include unique feature ID's (mz_rt), mz and RT
features <- imp_meta_omics_sep %>%
  cbind(imp_meta_omics$mz_rt) %>%
  dplyr::rename("mz_rt" = "imp_meta_omics$mz_rt") %>%
  dplyr::select(c(mz_rt, mz, rt)) %>%
  distinct() # remove the duplicate rows

# create a second data frame which is just imp_meta_omics restructured to another wide format
data_notame <- data.frame(imputed_omicsdata %>%
                            dplyr::select(-row_id) %>%
                            t())

data_notame <- data_notame %>%
  tibble::rownames_to_column() %>% # change samples from rownames to its own column
  row_to_names(row_number = 1) # change the feature IDs (mz_rt) from first row obs into column names

Check structures

# check if mz and rt are numeric
str(features)

## 'data.frame':    2425 obs. of  3 variables:
##  $ mz_rt: chr  "189.1598_0.513" "104.1071_0.537" "184.0946_0.54" "162.1126_0.545" ...
##  $ mz   : num  189 104 184 162 204 ...
##  $ rt   : num  0.513 0.537 0.54 0.545 0.589 0.591 0.595 0.597 0.611 0.612 ...

# check if results are numeric
head(data_notame, n = 1)

##                     mz_rt 189.1598_0.513 104.1071_0.537 184.0946_0.54
## 2 x5109_b1_beta_c18pos_65      5393.7170    173277.2800    51759.0040
##   162.1126_0.545 204.1231_0.589 265.2403_0.591 144.102_0.595 118.0864_0.597
## 2    144957.5200     35302.4730     25206.2950   496116.2200    213544.1900
##   337.0619_0.611 132.102_0.612 623.1355_0.614 116.0707_0.623 645.1222_0.627
## 2     54898.4920    54694.3200     11556.9730     33922.2100     12947.2550
##   293.0984_0.628 100.0757_0.629 160.0606_0.629 353.0342_0.629 172.1333_0.62
## 2    669172.9400     35459.4900     98662.8200     26301.6210    39135.1450
##   120.0808_0.631 166.0861_0.629 258.1105_0.632 310.2012_0.633 188.0707_0.633
## 2     42466.1330     49830.1250      8127.8880     22907.9510     38425.3800
##   629.1522_0.633 308.1855_0.633 286.2013_0.633 331.0535_0.634 167.0891_0.631
## 2     27773.8100      1071.1624     37006.4730    115179.8800      6466.6787
##   247.1441_0.636 315.0805_0.637 256.1695_0.637 277.1263_0.644 332.243_0.64
## 2     19227.8870    835308.9400       503.6229      1397.2515    6094.1104
##   398.3413_0.639 585.2705_0.643 660.0815_0.643 265.1183_0.643 272.1651_0.645
## 2       503.6229    106325.0160      6281.2650     25677.8980      1143.5956
##   235.0925_0.646 152.0706_0.642 682.0633_0.646 207.1109_0.641 287.1003_0.648
## 2     21110.9260       503.6229      4690.7150       503.6229     14763.2780
##   314.2326_0.654 464.1914_0.656 293.1101_0.657 338.065_0.618 377.2467_0.663
## 2     98041.8100      2922.8655     13778.0100     9382.8500     14033.5670
##   328.2481_0.675 634.4366_0.676 316.2483_0.679 316.2608_0.679 340.2481_0.684
## 2     18997.6460      3395.8367    172184.1900     10606.5730      8409.4020
##   648.4519_0.687 692.4782_0.686 736.5043_0.684 533.3253_0.69 706.4934_0.689
## 2      5077.3500      4088.0354      2706.9158      503.6229      3497.0635
##   330.2637_0.689 515.3654_0.688 588.4113_0.688 368.2792_0.689 750.52_0.687
## 2      9743.1250      3182.8100      1957.7994     10114.1220    2877.0784
##   342.2638_0.69 508.3578_0.689 346.0093_0.691 240.1585_0.692  643.38_0.69
## 2    34116.9340      3075.1228     25780.4180       503.6229    1043.2233
##   356.2794_0.691 363.2166_0.691 254.1387_0.695 344.2792_0.693 205.1221_0.621
## 2      5844.3740      7206.6630       503.6229      7690.0176      5093.7275
##   259.0944_0.697 616.1763_0.702 425.1649_0.679 423.1686_0.697  429.24_0.73
## 2       503.6229      4483.4760       503.6229       503.6229    1073.3726
##   346.0093_0.919 616.1763_0.902 383.1533_0.931 344.2793_0.927 466.3162_1.004
## 2     22806.3960      2304.9036       503.6229      8477.9200       503.6229
##   167.0854_1.05 308.1256_1.117 286.1437_1.13 373.2483_1.147 353.079_1.243
## 2      503.6229      1220.9514     9559.3440      2644.5095      503.6229
##   337.1048_1.268 315.1224_1.279 585.2701_1.361 450.3212_1.383 426.3213_1.384
## 2      1094.0752       503.6229     16384.2050      4375.3020      1517.1168
##   413.2797_1.415 399.2641_1.416 312.1597_1.413 463.2323_1.708 531.2196_1.712
## 2      1245.3229      1691.5208      1938.8829      1366.6835      1879.2894
##   526.2644_1.714 424.3418_1.822 163.0753_1.983 365.1356_1.988 343.1537_2.002
## 2      2830.3435     12478.9500      1823.2700      1293.2098      1224.5826
##   430.2589_1.992 400.3418_2.122 542.3237_2.234 426.3575_2.274 518.3238_2.281
## 2       503.6229     20248.2400     15779.0860     25978.0980     10080.8030
##   468.3082_2.293 516.3057_2.488 494.3238_2.494 590.3212_2.604 568.3394_2.605
## 2     36834.3630      6834.4434     41801.2000      7841.7600     42264.5470
##   445.2944_2.63 566.3214_2.69 536.2833_2.749 544.34_2.697 482.3238_2.701
## 2     2599.1511    56812.7030      2144.9417  368163.4700     43307.0300
##   1087.6686_2.703 1085.6532_2.71 1063.67_2.714 1001.6557_2.715 526.2925_2.719
## 2       8532.2020     11860.8980    45158.1300       5241.0960     19726.0880
##   1128.671_2.722 1045.6241_2.726 578.3923_2.734 522.3458_2.735 1039.6724_2.735
## 2      3874.5076       4286.0070      8663.1660    104104.3360     141075.4400
##   1050.6573_2.737 1580.9848_2.737 637.4547_2.737 520.3416_2.737 854.4963_2.738
## 2       3425.4797       2591.2930      9267.7600   2110536.5000      1008.1475
##   1061.6537_2.738 558.2953_2.739 801.9871_2.739 1051.1585_2.738 802.4888_2.74
## 2      37508.3400     25146.8550      6660.1260       4586.0674     5628.0415
##   1077.6264_2.732 542.3219_2.742 1321.82_2.739 428.3732_2.759 603.2923_2.783
## 2       2352.1426    216674.7800     1340.7731     22848.5140      3954.6320
##   524.2743_2.81 502.2925_2.81 570.3547_2.836 500.2744_2.854 478.2926_2.855
## 2     9483.8800    45643.7200     18142.3830     12592.0380     59690.5820
##   508.3393_2.879 568.337_2.996 546.3554_2.996 1041.6857_3.056 1041.6842_3.059
## 2      8472.4060    19313.4860     96251.1400       7538.0103       7538.0103
##   518.322_3.062 534.2952_3.067 766.4889_3.074 765.9874_3.073 554.3925_3.077
## 2   261189.1200     29635.3340     12595.9060     14793.6910     14094.7330
##   515.313_3.077 762.9789_3.079 1002.658_3.079 1029.627_3.079 1003.1594_3.079
## 2     6508.7183      3456.1096     12248.1160      7750.6987      12764.0290
##   1018.1281_3.082 1250.8236_3.081 1013.6543_3.08 1011.1465_3.08 1258.8122_3.08
## 2       3244.3508       3285.5662    134102.8100      4763.7650      4368.2915
##   1010.6446_3.081 1508.9859_3.081 1261.821_3.081 1018.6298_3.081 496.6809_3.081
## 2       3859.5142      23519.3830     12869.5620       4046.2460      4457.2540
##   1509.9892_3.081 583.4441_3.082 597.4596_3.082 498.346_3.082 627.4702_3.082
## 2      21969.5570     12773.0040      8819.3120   195381.2700     10046.3690
##   1487.0039_3.082 991.6743_3.082 496.342_3.082 613.4547_3.084 519.3249_3.064
## 2      15511.9660    879353.2000  4294420.0000     17320.1840     74408.8700
##   1026.6606_3.098 1339.8437_3.087 1017.6873_3.095 1039.6688_3.106
## 2       3632.0415        503.6229      57205.2580      12549.9610
##   844.5121_3.103 454.2925_3.202 560.3108_3.237 522.3564_3.24 639.47_3.238
## 2      1817.0374     27750.6600     17206.4860  1083035.8000    5109.1450
##   1043.703_3.238 544.3374_3.238 1065.6846_3.239 805.0108_3.238 572.3709_3.254
## 2     42585.4100    173100.4400      13565.8600      3742.7310     11761.4570
##   502.2902_3.355 480.3082_3.357 480.3446_3.381 482.3601_3.403 504.3422_3.406
## 2     12590.9080     50793.1600     30530.7100     30982.6910      6784.8174
##   548.3709_3.408 510.3551_3.445 438.2976_3.525 508.3757_3.535 808.0344_3.778
## 2     13561.2680     46895.3120     13126.1870     19657.0180      9954.2350
##   582.4238_3.778 808.536_3.778 1059.2224_3.779 1069.7165_3.778 1571.0972_3.779
## 2      6841.0654     8679.4720       2830.6848      38131.3050       1797.1025
##   524.373_3.779 562.3265_3.778 1593.0791_3.779 1047.7355_3.778 1331.8987_3.778
## 2  2038640.1000     20471.2420       3281.9902     137818.2500       2004.7372
##   611.4752_3.779 641.486_3.779 526.3772_3.778 1012.7169_3.78 1594.0825_3.778
## 2      7494.6733    14114.6920    105510.5160      3057.3100       3136.2773
##   546.3534_3.779 614.3398_3.784 550.3862_3.859 482.3239_3.884 504.3061_3.884
## 2    276992.3800     11940.7840     14665.8130     47507.3120     17674.9080
##   585.2706_3.939 1191.515_3.94 607.2524_3.94 299.139_3.94 686.3908_3.94
## 2    146034.2000    16599.9630    52378.4020   24249.0720     6995.1753
##   672.3751_3.941 1169.5332_3.94 1213.4964_3.94 629.2342_3.942 508.3754_4.012
## 2     18887.1700     25512.0620      6557.7050     15115.1260      6429.1084
##   510.3914_4.038 538.3859_4.051 664.5997_4.075 466.3289_4.123 541.3858_4.202
## 2     14236.2070      5810.8555       503.6229     17835.5300      2574.6082
##   561.4122_4.23 521.4196_4.236 539.4303_4.244 563.4268_4.262 317.2685_4.264
## 2    23843.7360     33456.5350     10956.2450     12931.3390      7061.0900
##   552.4018_4.301 593.4768_4.312 563.4273_4.415 619.4811_4.462 798.5661_4.516
## 2     10377.7250      5784.8290     18468.0040      3092.1760       503.6229
##   593.4757_4.54 712.5953_4.577 569.4277_4.639 568.4266_4.64 543.4017_4.622
## 2     4907.9043      1515.6571      5953.4795    12313.4640      4421.9320
##   617.4748_4.643 593.4762_4.644 595.4929_4.645 577.4817_4.655 515.3703_4.774
## 2     41282.3480      5630.0254     18788.2730      6552.4790      1753.6527
##   774.5634_4.81 822.5634_4.827 596.4154_4.906 620.4935_4.951 317.2685_4.946
## 2     1473.5468       503.6229      2267.3196      8821.8080     13718.4030
##   641.4722_4.95 619.4903_4.953 647.5118_4.967 669.4937_4.965 800.5792_5.005
## 2     4980.5635     20838.7250     16498.7270      3707.5247      1091.9003
##   798.5655_5.067 695.5093_5.094 673.5278_5.092 669.5575_5.14 610.431_5.16
## 2      1007.2459      8457.6890     31376.8890     2030.2296    4867.0110
##   597.5086_5.242 822.563_5.243 798.564_5.251 592.4686_5.28 683.5094_5.286
## 2      4058.8694      503.6229      503.6229     4339.0366      3237.3774
##   661.5282_5.288 719.5696_5.337 802.5954_5.397 752.5219_5.513 608.4637_5.649
## 2     11441.7550      3824.0588       503.6229      2898.7860      4542.1787
##   1372.06_5.66 834.5932_5.658 765.5124_5.66 1350.0784_5.66 697.5255_5.661
## 2    1504.1250      7477.3240    10079.4390      2835.2773    106392.7800
##   675.5442_5.66 713.5011_5.661 755.4833_5.662 1035.2944_5.66 834.5933_5.658
## 2   386197.2200      3942.7058      6335.6216      2901.3713      8029.4736
##   754.5378_5.732 678.5059_5.698 826.5945_5.722 828.5522_5.728 860.609_5.81
## 2      3039.8936      5521.1553       503.6229      1888.6975    6816.4756
##   791.5279_5.817 1424.0918_5.818 739.5144_5.819 723.5413_5.819 1074.3179_5.819
## 2     10553.4450       3385.8970      5583.9480    144145.2700       3523.3270
##   1402.1101_5.819 1403.1135_5.82 1425.0951_5.82 701.5604_5.818 1073.8159_5.82
## 2       4824.2750      5107.7783      2573.1562    599996.9000      2832.2780
##   1227.7565_5.834 804.5524_5.837 826.5347_5.837 1249.7383_5.838 778.5376_5.842
## 2      12203.0250     12628.0010      3870.6897       8835.8190     12066.2390
##   800.5194_5.843 614.3823_5.849 780.5532_5.895 781.5567_5.896 894.5999_5.902
## 2      2907.3804      3563.6924     11540.6230      6917.9070       503.6229
##   796.5458_5.943 774.5637_5.944 518.4564_5.944 532.4721_5.945 699.5412_5.971
## 2      3604.9233     13888.0150     11549.3370      7264.8643      3503.8652
##   677.5586_5.973 826.5348_5.993 677.5582_5.973 826.594_5.988 776.5195_6.001
## 2     13189.3320      2856.2268     13189.3320      503.6229     11136.4220
##   754.5373_6.002 1378.109_6.06 689.5597_6.062 711.5408_6.063 870.596_6.054
## 2     40443.7230     1011.0738    200887.2800     50749.2400     1772.1250
##   1460.0658_6.066 1419.0884_6.065 721.5835_6.066 749.5557_6.066 727.5745_6.067
## 2        503.6229       1109.9097      9074.2630      5750.4565     19276.9770
##   847.6527_6.068 721.5846_6.067 730.5383_6.068 752.5201_6.07 768.4931_6.068
## 2      2905.4746      9074.2630    114104.4140    27510.7620      1770.2836
##   854.5686_6.074 719.5692_6.151 741.551_6.153 1024.6773_6.157 1046.6591_6.159
## 2      6152.8900     15576.8950     4452.8680      10911.2160       6503.9814
##   836.4936_6.219 846.5226_6.22 873.6685_6.266 778.5354_6.223 756.5541_6.24
## 2      4037.4954     4128.2330      2879.3850     25760.3340   110201.8400
##   852.5506_6.226 830.568_6.228 852.5508_6.228 715.5745_6.252 814.535_6.26
## 2      5595.4194    18959.2660      5595.4194     15226.3955    3462.7990
##   792.5531_6.261 1282.9156_6.311 860.4936_6.313 870.5225_6.318 1192.8093_6.322
## 2     12696.3280       1923.9395      6466.2800      9003.2150       2011.7031
##   887.5125_6.318 803.037_6.321 855.6216_6.322 838.606_6.327 802.5357_6.323
## 2      6353.0870     3670.3958      3051.4343     1869.0016     91769.5300
##   867.6575_6.328 897.6684_6.323 1583.0843_6.325 1582.0806_6.324 818.5091_6.325
## 2      2372.9265     11467.2400       1582.3990       1568.4613      4040.8977
##   1332.9345_6.33 1181.3124_6.332 780.5549_6.325 1560.0983_6.326 1572.0922_6.339
## 2       503.6229       1025.9532    368834.2000       4451.9185        503.6229
##   1561.1021_6.326 911.6839_6.326 1083.7892_6.33 791.5397_6.411 1536.0989_6.339
## 2       4407.1304      6154.8230      2970.0203      6074.7397       2921.4050
##   1180.809_6.327 1586.1131_6.353 1537.1023_6.339 815.5406_6.313 1558.081_6.341
## 2      1127.5221       3110.2422       2659.3777      3513.5120       503.6229
##   856.5843_6.351 1584.092_6.345 808.5757_6.369 1563.1161_6.369 806.5698_6.37
## 2      4378.5490      1198.4238     29549.9180       2475.0942   195438.1200
##   937.6995_6.371 807.5729_6.37 1612.1288_6.374 1613.1329_6.372 1562.1109_6.352
## 2      2721.3022   102868.9500       1763.7870       1275.6945       3185.3523
##   896.5379_6.373 828.5511_6.373 756.5542_6.373 923.684_6.374 1513.1028_6.383
## 2      4877.0825     45829.2930    125456.4500     6563.7100       1131.3215
##   778.5355_6.376 873.6684_6.375 1512.0991_6.376 887.6837_6.371 783.5732_6.445
## 2     29409.0310      4166.9194        503.6229      1988.4071    331912.1200
##   691.5742_6.403 1586.1128_6.4 862.5091_6.42 1587.1171_6.406 790.5352_6.424
## 2      3770.0908     4523.0825     5067.8040       4852.6760     10887.0890
##   872.5379_6.428 804.5511_6.435 768.5536_6.434 857.6371_6.438 1195.8324_6.435
## 2      8783.2360    111106.5100     55822.7970      5338.6836       2865.3325
##   820.5246_6.44 899.684_6.44 869.6734_6.444 1551.1176_6.447 765.0535_6.446
## 2     6667.6587   17594.6720      3180.4214       3467.1973      6213.0625
##   913.6995_6.445 1155.8333_6.446 1156.3359_6.446 1588.1268_6.447
## 2      8412.6800       3080.6865       4607.5050       6853.0015
##   1588.1294_6.449 1550.1138_6.449 782.5712_6.447 1536.6212_6.45 840.6219_6.452
## 2       6853.0015       4199.6777    678390.0000      1190.5923      6660.0550
##   884.6064_6.452 1589.1333_6.45 1538.1147_6.455 1536.1192_6.444 1564.1312_6.455
## 2      9413.5800      5112.1597       5550.9785       1409.7110      19175.4530
##   1565.1348_6.456 1539.1179_6.455 1496.1206_6.463 1496.6252_6.461
## 2      18705.8030       4691.4404       2144.1648       2699.3071
##   1116.8381_6.462 1497.1271_6.467 1508.122_6.465 1507.1178_6.465
## 2       8629.7590       2977.1853     10895.9680      13234.0550
##   1472.1218_6.472 862.6247_6.471 1468.623_6.472 1468.1206_6.471 1471.118_6.473
## 2       3205.9070     29764.3610      5167.5264       5868.0024      5601.3022
##   1509.1328_6.475 1457.6299_6.479 1486.1407_6.477 1457.1286_6.478
## 2      11411.8890       4680.1343      61512.7800       5138.5570
##   1485.1374_6.478 1527.117_6.481 1565.1864_6.477 1526.1136_6.481 1510.1375_6.48
## 2      60516.3240      6122.2560       4547.9770       6177.3384      9231.8545
##   1483.1167_6.498 704.4038_6.503 704.3938_6.501 704.5181_6.5 706.5843_6.5
## 2       3701.0376      2979.1597      3859.7725    5754.9610   62212.0100
##   703.8986_6.501 703.9516_6.503 705.6376_6.502 703.5779_6.502 704.5113_6.501
## 2      4795.3620      4313.9077     12838.2060   3894128.2000      4318.4080
##   1469.1118_6.499 1406.1439_6.504 834.7053_6.506 1448.1242_6.506
## 2       6197.3706     201903.2700      8837.9260      23742.5410
##   1418.1346_6.507 1447.1208_6.507 820.6896_6.508 1417.6332_6.508 1065.848_6.509
## 2      11429.7060      28251.1170     14094.6455      11612.3490      5100.8896
##   1066.3498_6.509 1469.1116_6.5 744.5538_6.51 1459.1207_6.512 1419.1345_6.509
## 2       7944.8300     6197.3706   130015.7600      21799.0680       4790.9395
##   1428.1239_6.513 1429.1272_6.513 1460.1239_6.512 1489.1055_6.52
## 2      38435.3200      39882.6250      19428.5960      3819.5625
##   1500.0993_6.518 1512.1013_6.516 758.5603_6.497 1444.1182_6.522
## 2       2863.6204       1807.7603     16470.0250       2425.5144
##   1451.0918_6.532 1077.3414_6.528 1411.1131_6.529 757.5569_6.495 1481.1047_6.53
## 2       1944.3883      19003.9920       4645.9330     54360.3800      4764.3140
##   1482.1063_6.531 1479.1431_6.532 1410.1118_6.532 741.5301_6.535
## 2       3807.7766       1477.4005       9581.1280     13782.2000
##   1463.0927_6.535 1413.0725_6.543 1443.1241_6.536 1431.0932_6.55 725.5571_6.544
## 2       1717.4335       1060.2296        503.6229      2014.5331    260720.4800
##   706.5381_6.548 1409.1106_6.553 1187.9262_6.553 776.579_6.552 823.6523_6.559
## 2     93600.5800       6711.3150       4113.9510    13343.0590      3093.4878
##   728.5196_6.564 794.5671_6.58 762.541_6.576 445.3676_6.577 795.5713_6.591
## 2     16527.3140     9425.6500     1313.9385    109687.9300      4672.0625
##   738.5064_6.603 1175.7015_6.637 1153.7198_6.64 822.6001_6.635 770.5692_6.675
## 2      2870.3188       6597.0938     11591.6800       503.6229     20639.8160
##   886.5092_6.697 1576.1291_6.701 751.5715_6.701 1192.8412_6.709 807.3448_6.714
## 2      6770.4907       2247.0337     44493.5550       4918.5146      2046.7420
##   1193.3431_6.71 1232.3347_6.716 1557.1328_6.71 1585.1303_6.711 1558.1357_6.713
## 2      5770.2754       7525.5645      4170.9136       2856.0566       3915.7880
##   818.0604_6.711 1458.1721_6.714 1585.6318_6.711 729.5912_6.714 1635.1157_6.712
## 2      4732.0010       2063.3494       2523.9658    306413.6000      17849.0180
##   1634.1123_6.713 923.6842_6.713 1536.1554_6.715 1535.1521_6.713 806.5725_6.713
## 2      15352.7360     32366.5020      17665.0300      17672.5080   1990778.8000
##   896.5381_6.715 1586.1251_6.718 1597.1201_6.716 893.6733_6.713 1623.6198_6.714
## 2     12253.8570       2667.9382       2787.4663      6461.1250       3466.6730
##   1596.1232_6.714 808.6366_6.716 937.6999_6.715 1612.1323_6.715 907.689_6.715
## 2       2560.0590     11384.0690     19629.9690      77630.0200     4811.0470
##   1624.1217_6.715 1613.1357_6.716 1596.6247_6.715 1231.8327_6.718
## 2       5639.5933      80207.5700       2719.5640       8712.6875
##   1540.1229_6.721 1597.613_6.72 1539.1194_6.72 1538.1166_6.72 829.5547_6.726
## 2      18157.9750     3647.1614     30808.1970     31225.4450    112573.4140
##   1462.1391_6.719 806.973_6.716 1624.6226_6.715 1461.136_6.721 1598.1117_6.724
## 2       3168.6880     1876.8628       3109.7778      3381.0615       3605.3027
##   844.5247_6.723 881.6374_6.724 1465.1023_6.726 1194.3346_6.723 1561.1011_6.726
## 2     12924.3340      8552.9310       3474.7380       4337.5340       5940.2400
##   732.5542_6.723 1560.0999_6.725 828.5517_6.726 1464.0995_6.726 863.6839_6.733
## 2    276055.3800       6536.0320    217082.3100       3964.6438      3554.8188
##   1486.0818_6.746 830.5605_6.803 1588.1294_6.745 819.6577_6.728 810.5875_6.727
## 2       1056.5840     36147.8950       6560.4404      2858.3442     16855.0160
##   864.6223_6.744 791.5433_6.743 849.6682_6.746 1570.1174_6.785 1571.1203_6.766
## 2      9567.5260      4449.9565      8039.0127       2437.0930       1673.1512
##   1232.8362_6.721 1564.133_6.867 752.574_6.803 1639.1441_6.788 764.5588_6.812
## 2       6162.0273      1592.2296    22218.8850       1738.5306     19950.1780
##   1389.0311_6.813 446.3707_6.814 829.5548_6.746 848.6162_6.816 1547.1209_6.83
## 2       2245.2970     15048.3770    112573.4140      1549.0123      1851.0347
##   445.3675_6.816 786.5403_6.817 1610.1103_6.827 816.5509_6.821 794.5677_6.822
## 2     44383.0470     13006.0770       2360.6768     12866.7840     23135.7680
##   770.5691_6.825 756.5471_6.823 752.5739_6.808 754.5356_6.825 830.5623_6.824
## 2     45051.0200     13398.2500     21862.4500     82551.0600     44440.2580
##   775.6384_6.825 1638.1373_6.816 751.5717_6.812 889.5277_6.826 792.5512_6.827
## 2      9095.5300       2424.0745     41401.2460     12069.7360     31182.0450
##   1189.3305_6.84 862.5095_6.827 429.3727_6.826 812.5558_6.826 740.5662_6.833
## 2      2290.8455     12648.2920     29734.8850      9023.7230     11398.9930
##   717.5903_6.828 827.6071_6.849 718.5932_6.828 725.5564_6.829 788.554_6.828
## 2     31804.7540      6890.6720     18248.2910      6774.0830     7176.1797
##   824.5758_6.83 1233.3393_6.794 703.5745_6.822 1546.1205_6.851 806.6235_6.835
## 2     5052.6846       2624.7170     17905.9220       2267.5580      8408.0940
##   1664.1589_6.841 1341.0272_6.83 1536.1083_6.846 802.5963_6.831 790.5738_6.832
## 2        503.6229      1807.0807       4108.0620      5939.1426     14675.8810
##   791.5771_6.835 870.5402_6.834 854.5671_6.833 762.5403_6.835 742.5356_6.834
## 2     10014.8450      5618.0310     95410.9400      5182.0020     11383.8750
##   1363.015_6.835 742.5728_6.833 1365.0287_6.836 800.5726_6.827 793.556_6.829
## 2      4216.7646      6285.1150       6167.1187      2866.0864    19775.4650
##   1339.0138_6.829 820.5836_6.837 755.6054_6.838 720.5531_6.843 949.6995_6.841
## 2       1964.2231      3550.4966     12663.0290     20646.0000      3175.7224
##   1207.3311_6.841 832.5855_6.839 740.5626_6.841 780.5519_6.84 963.7151_6.844
## 2       3592.2368    215492.3400     11398.9930   268613.7800      2147.8960
##   805.617_6.84 1170.3294_6.844 740.5609_6.842 727.5724_6.844 1169.3422_6.843
## 2   23781.8240       5499.6943     11398.9930     71935.2700       2549.3354
##   1208.3368_6.844 1221.342_6.847 796.5247_6.843 1208.8392_6.844 804.553_6.847
## 2       8168.1490      8990.7580     13196.6550       8508.4030  1490166.8000
##   1220.8404_6.847 1171.3299_6.847 822.528_6.849 1490.1152_6.846 706.5938_6.852
## 2      11420.0000       6874.2793    12749.6440       2711.4253     67258.3750
##   1637.1286_6.848 820.5251_6.85 1537.126_6.857 1550.1223_6.858 705.5909_6.851
## 2       4997.2275    56154.9450      5891.6943       4848.1133    164496.8400
##   1611.1156_6.852 1144.8407_6.848 1599.1159_6.853 1511.145_6.854 1601.625_6.853
## 2       4880.6240       2717.0547       4693.4136      3669.1072      4794.3223
##   1549.6231_6.854 1157.3434_6.852 1145.3428_6.85 1171.8322_6.85 1510.1351_6.854
## 2       3179.1572       6450.3070      2907.8354      9182.7550       3332.9200
##   1599.6188_6.853 766.5727_6.851 1183.8334_6.853 1509.1326_6.853
## 2       5672.6940     17519.1270      34739.0980       3473.4220
##   1600.1221_6.854 1184.336_6.855 1611.6196_6.855 1612.6219_6.855
## 2       7925.3870     26521.1100       4443.6200       3518.4224
##   1600.6232_6.854 1580.1025_6.858 1536.6252_6.856 1601.125_6.853
## 2       6881.6855       3157.4084       3068.8670      7206.6090
##   1536.1177_6.855 1195.8336_6.857 833.6399_6.878 1157.8451_6.853
## 2       4108.0620      68607.1800      5561.7715       7063.8403
##   1573.1314_6.859 1548.6249_6.858 1523.1232_6.86 1537.1276_6.858
## 2       2732.1280       4955.4800      2464.6990       5891.6943
##   1589.1303_6.863 826.6051_6.862 1562.1129_6.862 1574.6139_6.862
## 2      19960.3400     12937.0000      22721.0200      20510.0120
##   1581.0989_6.863 1553.1294_6.871 1575.1173_6.863 1587.1165_6.864
## 2       2159.3071       5932.7344      29707.4590      73110.5100
##   1586.1131_6.864 1487.1515_6.871 1563.1166_6.865 1575.6183_6.866
## 2      56394.6500      11787.8680      28489.4200      38242.0900
##   1592.5935_6.867 1563.6184_6.864 1514.1154_6.867 1576.1212_6.867
## 2       3451.8123      17199.9920      11699.0390      35512.4450
##   1548.1292_6.867 1572.126_6.868 1580.5988_6.86 840.6221_6.868 1616.1518_6.868
## 2       6371.3115      2966.9448      2407.3428     19593.6020      13179.2820
##   1615.1494_6.868 784.5042_6.876 785.0201_6.879 1593.594_6.87 1594.0976_6.869
## 2      24269.1150      6912.2266      7153.8850     3847.8716       3865.2078
##   1502.1143_6.871 1614.1454_6.868 784.7847_6.875 1564.621_6.871 857.6374_6.871
## 2       4114.4297      23931.6930     22815.4160     15694.0110     15725.6570
##   784.4182_6.876 784.3687_6.876 783.1226_6.878 783.4174_6.877 1593.0936_6.87
## 2      5728.1390      5230.8975      5396.5073      9845.5520      3322.1904
##   1515.1185_6.869 913.7001_6.872 1487.1511_6.872 783.1504_6.876 785.5792_6.874
## 2      12058.3870     55061.8550      11787.8680      5308.9165    142187.2500
##   899.6847_6.873 783.3656_6.875 784.779_6.877 782.9105_6.881 784.6308_6.874
## 2     77207.8800      9307.4820    22815.4160      9752.2200     31362.0040
##   783.332_6.875 783.4393_6.874 783.3243_6.874 783.2199_6.876 784.3942_6.875
## 2     7112.8164      9775.9930      9051.5180      6609.1133      5407.6475
##   1577.1237_6.867 784.4291_6.877 783.1601_6.876 785.0064_6.878 783.1327_6.878
## 2      17270.2850      6519.6690      5370.0770      7101.7363      5553.3584
##   782.5732_6.875 783.2011_6.878 1488.1552_6.873 783.5024_6.875 783.2594_6.875
## 2   5118051.5000      5738.1084      10344.3640     16438.4000      8841.0060
##   783.2926_6.876 783.431_6.876 783.2519_6.875 783.2153_6.875 783.3975_6.877
## 2      8879.7370    11415.3190      8841.0060      6609.1133      9822.5610
##   783.3843_6.877 784.4472_6.876 794.0606_6.876 783.4057_6.877 785.0389_6.88
## 2      7887.0366      7024.9863     10706.0180      9822.5610     7854.3130
##   785.049_6.877 783.2313_6.877 784.4911_6.877 786.5814_6.876 782.9653_6.878
## 2     6642.3755      7012.7370      7622.0440     29706.6050      6474.7227
##   783.067_6.877 784.5306_6.877 783.1442_6.879 783.0991_6.879 784.3802_6.879
## 2     5600.5674      7788.1030      5385.0420      4890.1500      5426.7188
##   784.3306_6.878 782.9942_6.879 783.3522_6.877 783.1872_6.878 784.3478_6.878
## 2      4889.5190      5416.9478      7914.3000      6745.3380      6518.7840
##   783.3414_6.876 1562.1131_6.863 784.4086_6.876 782.5279_6.881 784.3757_6.877
## 2      8348.8110      22721.0200      5975.5410     14215.5460      5426.7188
##   1590.1432_6.882 1549.1336_6.872 784.9793_6.877 1548.1302_6.87 1566.2163_6.888
## 2      22002.9630       6424.1000      8130.7410      6655.7446      22692.1950
##   1564.1347_6.888 897.7049_6.893 1569.1509_6.94 883.6894_6.894 869.6738_6.894
## 2     850852.2500      6647.4990     23776.1930     17272.9080     20351.4360
##   1525.1338_6.906 1565.1377_6.892 784.6324_6.878 1592.1507_6.918
## 2       2977.8179     861451.7000     31362.0040      12488.2100
##   1552.1254_6.917 1463.1508_6.915 1172.8405_6.921 1172.3364_6.884
## 2      12711.5050       2801.7778      10840.8470       9370.1010
##   1551.1225_6.925 1550.14_6.908 782.0605_6.928 1591.1484_6.916 1551.6195_6.93
## 2       9737.1880     4848.1133      8147.8960      22023.9100     11321.2010
##   1540.1336_6.931 1542.2142_6.931 1541.137_6.931 1541.2129_6.933
## 2     486983.6600      11582.8310    465805.6000      18545.8790
##   1524.1325_6.944 1160.8402_6.939 1539.62_6.944 1160.3363_6.993 890.7034_7.007
## 2       3996.6003      12344.8990    13607.3100      11612.7120     24366.7950
##   846.6771_7.001 876.6878_7.013 759.4494_6.981 759.2363_6.979 760.4788_6.981
## 2     14237.4900     26663.7320     13573.4520      9096.3140      8931.8230
##   759.4107_6.98 873.7049_6.978 760.3659_6.98 759.1825_6.981 761.2418_6.982
## 2    12453.7660      8745.5550     7240.0250      8073.8936      7632.6235
##   761.5791_6.979 758.6931_6.98 760.5104_6.985 759.3448_6.983 758.5734_6.982
## 2    159828.1700   151150.4800     10110.6830     12389.0630   5924760.5000
##   759.3553_6.982 759.5018_6.981 759.317_6.981 759.3092_6.981 760.4052_6.983
## 2     11545.6080     19481.5640    11961.7450     11055.1080      7653.5890
##   759.3828_6.982 760.4925_6.98 759.4259_6.982 759.3339_6.982 1516.1361_6.982
## 2     13261.5640     9171.5510     12957.3940     12457.1140    1410985.4000
##   759.2062_6.983 759.391_6.982 758.9593_6.983 759.2797_6.981 760.6287_6.982
## 2      9527.5670    13356.7440      7871.1600     10960.3310     37455.2970
##   760.421_6.983 759.2558_6.98 760.5281_6.984 761.2551_6.982 1517.1828_6.983
## 2     8018.0870    10437.2280      8964.4430      7059.6700      70068.8050
##   759.2209_6.98 760.3904_6.983 759.1347_6.985 758.9871_6.983 759.2864_6.986
## 2     8099.9546      7451.4630      7856.2646      7519.8735     10960.3310
##   759.1729_6.983 759.1221_6.983 759.0903_6.983 759.243_6.983 760.4378_6.981
## 2      8494.0900      8485.6700      9391.7260     9508.1700      7236.2650
##   859.6895_6.984 761.0316_6.983 759.1094_6.983 760.5329_6.983 759.058_6.985
## 2     23681.3400      9723.2900      7969.4097      9791.5930     7774.2285
##   758.5284_6.987 759.4041_6.982 760.9991_6.984 760.3462_6.982 760.2985_6.984
## 2     16056.2940     13356.7440     10946.2920      6063.8203      5087.1553
##   759.2652_6.984 759.0221_6.985 760.7903_6.983 760.2926_6.982 759.1449_6.983
## 2     10437.2280      7333.5530     25374.4570      5774.9730      6841.0490
##   760.3968_6.983 760.3759_6.985 760.6318_6.982 1518.2168_6.99 748.5833_7.03
## 2      7653.5890      7195.2363     35895.7270     27325.6560     1124.0197
##   761.0061_6.983 1528.1243_7.002 1148.8422_7.002 1527.6219_7.002 760.5406_6.984
## 2     10946.2920      18321.8710      18726.2700      16791.1300      8467.7660
##   1475.1518_7.004 845.6738_7.007 1476.1552_7.005 889.7001_7.009 769.5595_7.011
## 2       7617.8633     23587.6600       8129.0005     40159.9000     14592.2690
##   770.0615_7.009 1564.1269_7.052 718.5934_7.015 717.5903_7.018 875.6845_7.017
## 2     14226.6720       9514.4980     18629.5940     38487.5270     49967.1400
##   1523.0877_7.024 1478.1161_7.023 1522.0846_7.024 1479.1185_7.024
## 2      11077.4370       5792.9300      12143.6690       4028.0125
##   1568.1537_7.025 1567.1512_7.025 1538.1135_7.026 1539.117_7.026
## 2      73506.9200     138715.3100      38153.0500     43548.2500
##   1566.1476_7.026 1553.1308_7.035 1159.3314_7.035 764.5222_7.036 720.5532_7.039
## 2     146112.4200       8087.8223       9756.1900     24172.5860     14088.0380
##   1550.1462_7.021 1552.6273_7.034 1159.8332_7.037 1544.0675_7.037
## 2       3413.4805       8553.4920      15630.4540       4475.8433
##   1545.073_7.035 833.637_7.039 1173.8476_7.044 781.62_7.043 1572.101_7.045
## 2      3244.0056     7982.1294       6445.1055    5688.7090      2317.2297
##   780.5518_7.046 1173.3456_7.052 811.5943_7.054 1184.8408_7.054 809.5892_7.057
## 2    183253.7200       5460.8657     19779.6040       8766.1340    291041.7800
##   1617.1644_7.055 808.586_7.055 786.504_7.061 1616.1606_7.056 1578.1434_7.057
## 2      11359.0870   588403.0600     3929.7473      11933.2320       6161.7120
##   792.5893_7.058 858.5992_7.06 939.7152_7.063 848.5388_7.066 1185.344_7.069
## 2     15391.7010     2558.9810      7418.6600      9512.5550      6107.9320
##   925.6997_7.067 1172.8404_7.074 830.5667_7.072 1197.3456_7.08 1578.1423_7.058
## 2     11909.1820       3174.1330     70597.1900      3549.9673       6161.7120
##   1593.1638_7.133 1577.1364_7.091 1564.1272_7.069 794.0604_7.102 793.5586_7.102
## 2      20253.2640       2604.5760       9514.4980      7002.1370      6752.8570
##   1565.1315_7.095 1577.6354_7.106 731.6047_7.112 1545.1554_7.122 1185.8481_7.12
## 2       7596.2305       2974.8100      5290.3833       7436.1274      7499.9090
##   1186.3509_7.119 1174.8563_7.123 1543.1503_7.123 1542.1466_7.121
## 2       5486.9070       3801.6438      33650.1500      35511.2500
##   1566.6417_7.129 1544.153_7.124 1590.143_7.137 1506.089_7.132 1614.1417_7.109
## 2       2229.7130     18147.6930     14288.6090      1726.0093       4281.4746
##   871.6892_7.141 1592.1596_7.145 1594.1676_7.138 785.5418_7.137 1507.0917_7.135
## 2      8669.5390      26156.4300      11131.2230      2624.5994       1453.5707
##   1187.8642_7.137 1579.649_7.139 1187.3609_7.137 885.7047_7.143 842.6373_7.146
## 2       3963.2363      3174.7930       3975.4958      5349.6465      8487.8270
##   1568.1618_7.14 1591.1473_7.14 1580.1526_7.144 834.6001_7.175 784.5879_7.141
## 2     64757.8750     15067.0070       3506.3062     24930.0000   1692172.1000
##   1198.856_7.141 915.7154_7.143 901.6999_7.143 787.5943_7.141 807.5705_7.147
## 2      8771.7010     17416.8220     30570.2270     41582.9800     99010.7100
##   1569.166_7.14 822.5404_7.148 806.5675_7.192 1574.148_7.172 1575.1514_7.174
## 2    62605.8000     10181.6010    197554.6900      3456.5688       2419.8360
##   859.6529_7.212 925.6996_7.166 445.3675_7.185 1530.1458_7.188 1531.1493_7.186
## 2      8776.9590      8828.3290     12413.9570       6399.9870       4770.6800
##   874.5538_7.197 692.5581_7.191 790.5744_7.193 1598.1451_7.197 1554.142_7.183
## 2     12562.0710      7282.0550     32107.7170       1604.7759      2480.2630
##   831.57_7.208 812.5561_7.198 907.6886_7.2 746.5692_7.204 830.5666_7.214
## 2   28144.8570      7984.7334    1989.6963     95167.7200     48927.3360
##   768.551_7.206 748.5272_7.208 842.5663_7.213 820.5846_7.213 864.5249_7.21
## 2    16492.4940      5183.9150      6109.2880     24081.3870     6938.9453
##   827.6266_7.218 1548.0963_7.222 762.5038_7.219 740.5222_7.221 743.6054_7.227
## 2      3752.6655       3244.9707     13752.5580     42976.8600      7719.2812
##   1546.0812_7.232 1547.0845_7.233 856.5819_7.24 1524.0983_7.244 1525.1027_7.243
## 2       3483.6980       3488.1213     6638.0005       5453.5280       4776.9340
##   807.5706_7.257 834.6002_7.257 724.5272_7.284 797.5882_7.268 842.638_7.268
## 2    118741.4600     39144.8440      1910.8232     35788.4500    10514.0000
##   796.5845_7.27 1542.1462_7.263 1581.1632_7.283 806.5675_7.265 874.5539_7.248
## 2    72564.8600      10236.8920      12460.1220    221178.7000     12604.8080
##   1566.1444_7.237 859.6529_7.266 822.5405_7.274 807.0682_7.274 1604.1571_7.283
## 2       6496.6960      9165.1560     13790.8620     14134.1330       4398.7830
##   902.7033_7.285 1198.8563_7.281 1187.8649_7.285 1580.159_7.285 901.6999_7.285
## 2     22156.0230      15122.6640       8691.5720     16503.8630     43110.0430
##   1590.144_7.285 1579.6508_7.286 1199.3591_7.285 1602.1511_7.303
## 2     25340.2340       6353.3730      13333.3610       3956.8882
##   1619.1782_7.285 1187.3625_7.286 1591.1476_7.288 784.5881_7.29 871.6892_7.289
## 2       6223.6963       5950.3315      27671.9960  2985055.5000     12021.5920
##   788.5964_7.29 1569.2427_7.289 1569.1673_7.289 786.6508_7.289 785.452_7.291
## 2    13002.8090       7227.2820     169802.7700     14278.9120     3061.6868
##   1568.1636_7.29 915.7155_7.29 885.7047_7.291 787.5945_7.291 916.7188_7.29
## 2    183332.1200    26279.4590      9076.5380     69423.9300    16018.3640
##   784.9268_7.293 1591.6495_7.292 1199.8618_7.293 1602.6436_7.296 1500.102_7.296
## 2      3556.9736       7716.3160       8550.2400       3842.6375      8130.5380
##   1592.1607_7.297 1501.1056_7.297 1522.0815_7.299 1618.1731_7.3 1603.1486_7.299
## 2      53719.4200       6924.1850       3485.3882     7398.9080       6123.0910
##   716.5222_7.301 1593.1654_7.298 1210.8558_7.304 1210.3541_7.309
## 2     29371.9920      48039.5700       6494.1220       5208.9634
##   1614.1426_7.311 1615.1461_7.313 1616.1569_7.316 1211.3587_7.312
## 2       6480.6406       6684.4070       6791.0840       6283.5860
##   808.5857_7.318 795.5728_7.343 1617.1623_7.316 1557.1645_7.322 1556.1613_7.323
## 2    326688.6000     13748.4850       5146.9920       9951.8450      12318.7780
##   1616.1572_7.316 809.5888_7.318 939.7152_7.328 1601.1673_7.328 1600.1629_7.331
## 2       6791.0840    164733.8000      4276.1560       2296.2922       2488.0470
##   810.5975_7.409 925.6999_7.337 1577.1677_7.349 816.589_7.342 830.5666_7.346
## 2     71589.9800      6891.4443       4408.1323     9590.6030     40156.2030
##   1576.1637_7.357 792.5899_7.352 909.7046_7.351 814.5717_7.361 766.5377_7.38
## 2       4759.7980     52424.3240      3424.4446     10382.2705    27209.4060
##   1550.1483_7.401 1574.1454_7.378 719.6006_7.392 1572.1304_7.394
## 2       4758.8804       2446.9675      4816.7130       1660.7784
##   1524.1333_7.404 824.6209_7.408 1551.1527_7.401 1562.1493_7.418
## 2       1292.6350      2627.0017       5710.2900       2471.5918
##   1550.1497_7.403 718.5737_7.412 1533.1433_7.416 788.5563_7.415 766.5755_7.417
## 2       4758.8804      8682.3580       3260.2266     57011.7700    263404.9700
##   1532.1401_7.417 1562.1488_7.418 1563.1535_7.418 897.7047_7.419 883.6892_7.419
## 2       2746.2073       2471.5918       2596.2554      6101.3110     12916.7140
##   818.5668_7.42 1554.1324_7.421 796.5848_7.423 1577.1679_7.401 1576.1634_7.391
## 2    22529.6520       1357.0139    107920.9800       2470.9812       3383.8892
##   1539.1546_7.459 927.7143_7.45 812.6068_7.457 832.5817_7.451 811.6039_7.456
## 2       2163.3127     6838.9920     22156.7340     31705.1050     73096.5300
##   810.6006_7.457 764.5195_7.458 742.5377_7.462 1538.1514_7.472 881.5145_7.469
## 2    134224.0800      7721.0737     22778.2030       2572.7212      6062.2285
##   859.5323_7.471 840.5868_7.472 876.559_7.475 818.6053_7.48 1497.1674_7.486
## 2      5798.9062      8956.4600    11421.3570    37945.4260       1581.1414
##   807.6359_7.483 750.599_7.485 1583.178_7.489 1582.1737_7.485 1583.1777_7.489
## 2      3831.0168      503.6229      2480.3840       2310.0151       2480.3840
##   1508.1468_7.513 794.568_7.494 889.6996_7.495 1541.1852_7.515 772.5855_7.501
## 2       3585.3577    38519.9100      7052.8890       3199.9272    226832.1600
##   1500.1349_7.508 1509.1474_7.507 753.5881_7.505 1544.1615_7.508
## 2       2126.2856       2092.0752     85613.6950       4649.3955
##   1545.1646_7.511 1503.1775_7.514 1504.1822_7.513 822.5994_7.509 731.6073_7.511
## 2       4911.4050       6527.6740       3927.5327     13095.0530    454151.2800
##   1463.2071_7.511 1462.204_7.513 1515.1602_7.514 1514.1517_7.514
## 2       3124.4854      3205.0034       4324.0240       4498.1920
##   1564.1662_7.508 1473.1717_7.517 1474.1752_7.518 859.6891_7.519
## 2       2016.1738       4781.7980       3421.7024      8154.2960
##   1507.1493_7.519 1506.1473_7.519 764.5566_7.52 873.7045_7.52 1508.1474_7.516
## 2       6633.2515       6062.3270    31794.8850     4982.9850       3585.3577
##   1487.1502_7.525 1485.1447_7.523 742.5753_7.523 1518.1461_7.531
## 2       2613.7031       2148.8591    191131.1200       2861.4202
##   1484.1411_7.523 1486.1496_7.524 1465.1678_7.525 1466.1708_7.525
## 2       2470.0022       1497.9453       6654.8980       6344.9556
##   1477.1393_7.527 1523.1825_7.536 1476.1365_7.528 1553.1686_7.544
## 2       6809.4550       2414.2344       6057.2340       3506.8452
##   1526.1519_7.532 1534.154_7.527 1527.1556_7.532 1541.1773_7.535
## 2       4657.5740      2007.2792       3840.5676       3371.8267
##   1518.1432_7.534 1468.1317_7.534 1535.1558_7.533 734.5702_7.535
## 2       2861.4202       6072.6187       2250.0254    353520.0300
##   1469.1342_7.535 865.6997_7.537 851.6839_7.537 756.5512_7.537 821.6735_7.538
## 2       5004.6157      5414.0570     10778.7380     54331.4180      4345.4893
##   1479.137_7.538 1490.1052_7.54 1540.1625_7.542 804.5937_7.544 794.5862_7.544
## 2      2990.7666      2127.0884       3679.2427      3675.0496     15818.0250
##   1510.1552_7.549 1511.1587_7.551 1482.0893_7.554 1552.1644_7.555
## 2       4226.1150       3603.8474       2527.3809       3953.8550
##   835.5327_7.555 1499.1861_7.555 852.559_7.556 792.5899_7.556 1502.1505_7.558
## 2      4495.5090       5060.1533     7433.6323     79604.4140       8881.8440
##   1503.1576_7.556 1500.1884_7.555 857.5144_7.562 1495.1496_7.564 849.6468_7.572
## 2       8585.5010       4000.7683      3715.0370       2497.3570      1906.3047
##   1561.1726_7.566 814.5717_7.564 748.5274_7.569 1496.0473_7.572 1537.1733_7.573
## 2       2592.5684     13302.2200     33318.0080        503.6229       6685.3300
##   1517.1166_7.575 1516.1139_7.574 1560.169_7.579 1536.1705_7.575 770.509_7.576
## 2       2530.5850       2080.4326      2282.4321       6401.7490     9558.0200
##   768.5909_7.579 1578.1799_7.626 899.7204_7.586 835.6299_7.591 790.572_7.588
## 2    425445.9700       4914.7740      8112.9507      3593.3400    71291.9200
##   885.7047_7.587 1494.1454_7.592 806.5656_7.605 1512.1627_7.554 786.5917_7.604
## 2     18110.2000       2098.2405     15177.7750       2484.3500     12606.6910
##   1491.1814_7.626 795.6347_7.605 1579.1825_7.633 832.582_7.631 941.7308_7.633
## 2       2028.2305      4027.3157       5770.3623    32799.2770      3788.4329
##   902.5744_7.633 812.607_7.646 811.6042_7.645 810.6013_7.646 1578.1793_7.636
## 2      4986.0996    33542.0430    107926.2800    204152.2500       5323.6300
##   850.5541_7.648 1544.1549_7.686 1592.1591_7.656 1181.7509_7.659
## 2     13692.6510      23416.9530       3815.0010       3838.7810
##   1187.8641_7.656 818.6355_7.665 1545.1606_7.669 744.5901_7.661 816.587_7.665
## 2       3432.4690      8476.7350       9823.2010    122544.6400    23278.8140
##   1526.1435_7.632 1528.1659_7.665 1529.1702_7.665 782.5674_7.669
## 2       3219.6997      13098.2320       9945.9770    204366.1700
##   1605.1979_7.674 1571.181_7.67 1570.1764_7.672 1570.1773_7.672 911.7203_7.674
## 2       3559.1594    25507.2810      26085.3030      26085.3030      8083.8223
##   1604.1948_7.675 798.5402_7.677 1163.3585_7.687 1538.1799_7.62 1539.1868_7.674
## 2       4078.1277     12759.4400      12428.9020      3300.3145       3109.0042
##   1505.1707_7.678 1576.1618_7.678 1504.1669_7.68 878.7033_7.717 925.736_7.685
## 2      20141.2340       4844.1074     22547.9220     25510.8300     4708.0386
##   1558.1829_7.689 1162.8568_7.691 794.6062_7.691 1558.1819_7.689
## 2       7927.0690      16056.3570    297145.3800       6884.3910
##   1588.1988_7.697 1548.6599_7.695 1589.2021_7.7 1543.1477_7.702 1554.1833_7.701
## 2       4674.9470       3720.1523     5487.4917      43559.9200      71208.0300
##   1571.1809_7.671 1555.1868_7.702 1542.1448_7.703 1546.1714_7.763
## 2      25507.2810      70354.0300      40527.9020       6810.4053
##   1543.1481_7.702 1151.8656_7.71 771.5762_7.712 1531.6527_7.711 1151.3639_7.711
## 2      43559.9200     17830.4570     17433.5570      13362.9770      11389.1040
##   1544.1542_7.691 761.302_7.716 1532.1582_7.713 861.7048_7.713  877.7_7.713
## 2      23416.9530     4584.8213      12936.2470     20699.1840   49098.1300
##   1542.645_7.698 760.9218_7.716 1510.1192_7.742 762.5251_7.716 761.5191_7.716
## 2      9268.2520      6645.4194       2446.1653      5528.1370     10925.8580
##   847.6893_7.716 761.3781_7.717 762.6491_7.715 760.9763_7.718 761.4062_7.717
## 2     24082.5180      5340.2750     22571.0400      4674.2236      6146.4950
##   761.329_7.717 761.0763_7.717 761.4545_7.719 760.5883_7.719 762.5433_7.717
## 2     5142.3794      3327.1802      6169.5625   4489468.0000      5709.7173
##   761.4437_7.717 761.1927_7.717 763.5945_7.717 1521.1684_7.718 1520.1651_7.717
## 2      6169.5625      4225.2026    111291.4000     488071.2000     529120.6000
##   891.7156_7.718 761.3681_7.718 1531.1683_7.726 1535.1253_7.718 1534.1209_7.719
## 2     34318.6200      5340.2750       6923.9043       5424.9260       6476.6000
##   761.2595_7.716 761.4141_7.718 760.5459_7.719 761.3174_7.717 1583.089_7.72
## 2      4221.0815      5045.6753     11911.6570      4070.7178     7703.6436
##   1584.0928_7.721 762.5032_7.719 1521.243_7.719 1629.2009_7.725 1628.1971_7.724
## 2       7824.3150      5498.3190     19060.6230       6422.5650       6269.8535
##   774.5423_7.725 1608.1354_7.743 1568.1599_7.728 1542.1456_7.705
## 2     11221.3160       1272.1986       5344.1250      40527.9020
##   1568.6597_7.736 1569.1611_7.737 1569.1618_7.737 1595.1823_7.739
## 2       6495.0503       7725.8887       7725.8887      98361.6800
##   1594.1789_7.739 1588.1975_7.7 1188.8722_7.741 1597.1881_7.738 1579.6524_7.748
## 2     107510.5860     4674.9470       6300.3110      22405.0430       4470.2580
##   1580.1749_7.748 835.655_7.749 834.602_7.751 1669.196_7.752 1668.1927_7.752
## 2       5792.3900    11716.9440   448794.2200      7539.9023       7337.1504
##   1616.16_7.754 1617.1626_7.754 1199.864_7.757 1518.2006_7.758 1517.1985_7.759
## 2     7962.7446       8248.7310      6011.0376      14714.3430      14677.2840
##   965.7308_7.761 1540.1703_7.761 1591.2114_7.764 828.6106_7.766 1592.2118_7.764
## 2      4966.2070       5146.0073       2566.1387      6108.8945       3137.5660
##   818.6356_7.752 819.5737_7.769 951.7153_7.77 1531.181_7.771 1530.1768_7.745
## 2     10548.5900      5129.0654     8069.8920      7283.2686       7336.8540
##   757.6221_7.776 856.5823_7.778 798.5402_7.73 1484.1047_7.781 1559.1256_7.781
## 2    173236.7200     37189.6800    12759.4400      12958.6980       3212.6826
##   1558.1201_7.783 1485.108_7.781 1546.1749_7.78 1604.1937_7.739 780.5921_7.79
## 2       3719.1820     12755.6810      6810.4053       2881.0300    21050.6540
##   1506.0868_7.79 1547.1793_7.789 1507.0899_7.79 1580.1032_7.792 837.614_7.764
## 2      7164.4497       6038.4316      6378.9697       1895.6180    20684.0250
##   1481.1402_7.792 858.5914_7.792 825.6473_7.81 821.6234_7.811 820.6201_7.811
## 2       1800.8228      7071.5520     3993.2249     12139.4410     22466.3280
##   902.5743_7.805 724.5277_7.806 1448.0465_7.808 1470.0283_7.811 770.6049_7.812
## 2      5288.8380     82317.8750       2385.7983       1235.0146     53227.2660
##   825.6476_7.811 802.5717_7.81 746.5091_7.814 811.6292_7.819 770.6048_7.813
## 2      3993.2249     3054.8853     22823.3730      9954.6150     53227.2660
##   798.6003_7.83 860.6149_7.833 792.5888_7.837 1532.1012_7.882 820.5823_7.852
## 2    34042.7030      6221.1810     18303.9510       2098.2295      8507.3650
##   783.6368_7.847 805.6187_7.848 745.6202_7.852 818.6049_7.793 806.611_7.876
## 2     31443.3180      9091.6620     13998.8010      8690.4080     4951.1753
##   876.5695_7.88 878.5742_7.89 756.5904_7.889 748.5844_7.89 772.5245_7.894
## 2     7075.7820     5127.9660      9227.3960    18335.3610      9190.0830
##   1547.1803_7.856 700.5274_7.902 1486.1173_7.882 826.6326_7.901 820.6202_7.907
## 2       5399.4360     21380.6050       3084.0728      1115.1819     24864.7500
##   1578.178_7.924 854.6305_7.919 900.5692_7.918 1511.123_7.922 1510.1192_7.922
## 2      2589.2476      4165.8470      8471.4650      4173.4316       4908.7837
##   1558.1745_7.924 733.6212_7.929 1566.1816_7.932 734.6246_7.927 733.6215_7.929
## 2       3018.6914     26322.7480       1988.4016     13073.2260     26322.7480
##   1567.1909_7.931 1570.1778_7.936 848.556_7.937 885.6686_7.937 1544.2137_7.94
## 2       2143.8262      10944.2300    11359.5360      7438.5767      3019.1958
##   832.5829_7.939 1543.2104_7.94 822.5985_7.942 750.5434_7.943 1582.1177_7.942
## 2    190289.3000      2826.2402      7101.0186     42114.6330       6270.0050
##   1238.3816_7.948 1604.1947_7.949 1592.1862_7.949 1237.8801_7.949
## 2      11928.0460       5575.0215       3663.0583      12679.1630
##   811.0052_7.959 1642.1751_7.953 1643.1784_7.953 1605.1986_7.95 1561.1392_7.955
## 2      4200.2954      22001.8050      26935.0800      5968.8647      15708.4380
##   812.6663_7.96 822.0921_7.956 1227.3893_7.956 1560.1357_7.954 1632.1881_7.956
## 2    20483.7340     13487.0800       8973.3570      17124.4300      12940.0440
##   1591.1826_7.956 927.7157_7.955 812.5189_7.959 1226.887_7.956 821.5898_7.956
## 2       3305.5847     47009.2540      3487.0654     13389.3250     12952.9170
##   813.6106_7.959 1225.88_7.956 1226.3841_7.957 941.7313_7.957 1631.6823_7.958
## 2    105242.3700     9936.5750      11729.9080     34206.3320      12720.3600
##   1622.2784_7.959 897.705_7.958 911.7206_7.958 811.2977_7.959 810.9483_7.96
## 2       8327.5340    16258.5050     12577.9060      2953.2380     8446.5330
##   810.6041_7.959 1590.1749_7.958 1621.1991_7.959 811.4149_7.961 814.6127_7.96
## 2   3797555.0000       2573.6409     291290.3400      3379.2712    19150.5780
##   1620.1954_7.96 811.5374_7.96 811.4537_7.958 1621.2757_7.96 1631.1866_7.959
## 2    288335.1600     7926.8980      3951.1492     14227.1680      10745.7290
##   811.3445_7.959 812.545_7.958 1604.1946_7.949 1225.3783_7.964 1606.2002_7.968
## 2      2689.3547     4522.8545       5575.0215       8367.9040       4865.0280
##   1620.6864_7.972 820.5825_7.912 1215.3898_7.986 1580.196_7.986 1581.2003_7.986
## 2       7794.1480      7906.4290       8153.8105      8360.7840       8650.4210
##   1214.8875_7.988 1536.1354_7.989 1619.1791_7.991 1537.1392_7.989
## 2       9454.2780       7501.6694      25151.9940       7450.5186
##   1214.3846_7.993 771.6088_7.993 1618.1753_7.994 770.6056_7.993 1646.2057_7.997
## 2       9955.2040     21877.8140      19924.5230     45203.0550       9875.2490
##   1608.188_7.997 1597.1987_7.999 1607.6828_7.999 1596.195_7.999 1203.3894_8.009
## 2      9616.7790     238179.7200       8933.7910    237418.1000       8534.0230
##   1647.2105_8.001 1597.2743_8.003 1213.88_8.002 1607.1867_8.003 811.6598_8.009
## 2       9716.3730      10932.1640     8690.0570       9580.7330     15617.2100
##   1606.677_8.009 1202.8865_8.011 1556.1974_8.013 1557.2015_8.015
## 2      7183.5640      10947.6090       7585.3350       8709.0260
##   1625.2149_8.037 810.6564_8.03 1595.6827_8.019 1547.1806_8.02 1191.3906_8.062
## 2      12292.9170    27826.2540      14310.0230      6703.2856      15374.7880
##   1546.1772_8.02 918.7347_8.057 788.6651_8.049 1576.2067_8.049 788.4624_8.049
## 2      7400.9910     26834.8260     28974.5920      47163.5900      5721.8413
##   1202.3827_8.089 787.4429_8.052 787.1747_8.049 901.7361_8.046 788.4791_8.049
## 2      15047.3470      8742.4740      5992.7470      3997.7410      6112.3823
##   787.1568_8.05 1573.2005_8.05 787.0639_8.049 787.6075_8.051 787.4152_8.05
## 2     7667.6406    693332.7000      5572.2020   2972381.8000     7381.6720
##   787.4054_8.05 789.0553_8.052 788.4066_8.049 787.4705_8.05 887.7206_8.05
## 2    10284.9510      7108.6410      5121.1380     9619.8320    17676.8000
##   789.6103_8.05 787.2199_8.049 788.5214_8.05 788.6624_8.05 787.246_8.049
## 2   137969.9400      7135.8613     7705.8003    28974.5920     5294.0020
##   788.3967_8.051 786.6043_8.051 787.2651_8.051 787.4549_8.049 788.3339_8.051
## 2      4785.5156   5057976.0000      7128.3306      7922.1340      4397.5060
##   787.2508_8.05 1623.2097_7.994 787.2891_8.052 788.4473_8.05 789.0076_8.052
## 2     7128.3306      67560.0100      9301.7000     5244.8296      7598.0586
##   1574.2787_8.051 787.3251_8.05 790.6125_8.05 787.3567_8.05 787.1311_8.049
## 2      19970.3090     8355.4610    24993.3570     8801.4720      4919.2285
##   788.3678_8.051 787.0988_8.052 787.394_8.051 788.5317_8.052 788.5585_8.05
## 2      4608.7680      4709.1510     6810.9150      7705.8003     8609.7760
##   1572.1972_8.051 788.4344_8.049 1624.213_8.033 787.2834_8.051 787.1808_8.051
## 2     713128.7000      5311.0120     31627.4410      6348.5605      5992.7470
##   787.2998_8.051 787.0264_8.052 787.5336_8.052 787.3787_8.051 873.705_8.052
## 2      9301.7000      5334.6816     16162.1090      8637.3880    21801.8070
##   787.4344_8.052 786.998_8.053 789.039_8.054 786.5592_8.053 836.6167_8.053
## 2      8742.4740     5534.2007     8507.6920     13340.0500    164887.2800
##   837.6197_8.054 788.2955_8.051 917.7314_8.057 1574.2746_8.051 1625.2172_8.052
## 2     92194.1600      4771.1570     41920.6450      19970.3090      12292.9170
##   788.4171_8.052 787.5853_8.055 1577.208_8.052 1583.684_8.06 1190.8891_8.06
## 2      5784.9043     11014.6820     12456.6830    18803.3830     19758.0530
##   1584.1871_8.061 1504.14_8.066 809.6525_8.065 1542.1805_8.073 798.0928_8.066
## 2      21277.4650     6772.1220     35540.1330       1776.1273     17759.5550
##   1580.1886_8.021 903.7157_8.069 797.5914_8.07 1512.1341_8.076 1513.1387_8.078
## 2       7128.2500     54982.0400    20372.8700       4902.0320       4631.7810
##   1594.1759_8.08 1596.1936_8.011 1595.1798_8.081 1015.8403_8.077
## 2     37922.5080     237418.1000      43863.7500       4834.1772
##   1534.1768_8.083 1578.1482_8.087 1579.1512_8.088 1531.2124_8.09
## 2       2277.1152       8681.5790       7062.4985      2929.3164
##   1538.1459_8.043 1560.1917_8.095 1561.1945_8.097 1201.8801_8.097
## 2       4576.0415       6615.8640       6840.0800      19030.6540
##   748.5844_8.099 1601.1358_8.098 792.5534_8.102 838.6248_8.065 858.5976_8.109
## 2      7808.4106       2936.8900     20987.0800     27926.5350      9422.0000
##   1505.1673_8.109 844.6529_8.109 1568.1908_8.106 1504.1651_8.11 824.5559_8.11
## 2       4884.8370      9276.4620       2051.2266      6692.3066    11680.5940
##   861.6684_8.111 808.5829_8.112 745.6214_8.115 814.5355_8.122 876.5697_8.13
## 2      8620.0170    225450.6600     33230.9300      4392.0170    14577.0130
##   797.6166_8.131 812.615_8.136 782.6048_8.136 797.622_8.139 813.6188_8.138
## 2     13172.6850    55408.0800     13588.5570    13172.6850     30250.1230
##   718.5742_8.138 796.6199_8.138 740.5559_8.156 804.5876_8.154 803.6626_8.177
## 2     35830.4900     27612.8930      7921.2920      3303.4630      3644.0823
##   738.543_8.181 831.6353_8.196 794.6054_8.214 816.5873_8.216 772.5244_8.234
## 2     3907.0308      7501.3530     17375.9900      5054.9526      6606.4736
##   748.5258_8.233 750.5444_8.236 726.5431_8.239 726.5428_8.237 796.5824_8.278
## 2      5351.6120     14193.0200     10719.5840     10719.5840     15188.2330
##   742.5718_8.28 774.6005_8.288 720.5897_8.289 840.5869_8.291 818.6044_8.299
## 2    10703.9160     62710.8050     45988.0700      2605.9014     11294.8580
##   812.518_8.311 1559.0847_8.317 790.5354_8.318 1536.1002_8.319 869.6735_8.319
## 2    11549.1290       3963.9385     45920.6330       5456.7140      8030.8490
##   855.6577_8.319 1558.0818_8.319 768.554_8.322 766.5713_8.323 1537.103_8.32
## 2     10435.9850       4421.0864   137888.3600      9783.3010     6037.8916
##   861.7043_8.325 1512.129_8.321 744.5895_8.327  859.53_8.35 1603.1463_8.354
## 2      3211.8820      1559.1877     44567.2500    5301.6504       4844.4280
##   1602.1428_8.354 1581.1647_8.356 771.6366_8.357 577.5185_8.358 1580.16_8.36
## 2       4260.9030       6483.5720     19273.0550      9046.0080    6051.5376
##   837.5477_8.363 854.574_8.362 844.6193_8.384 1556.1852_8.372 796.621_8.383
## 2      6388.8820     5900.6200      2343.3713       2861.2085    42591.8100
##   1598.2072_8.389 1599.2108_8.39 797.6421_8.39 1609.235_8.392 870.6724_8.39
## 2       2189.4500      2292.6287    22040.9400      2434.6880     8020.4740
##   798.6518_8.391 768.5859_8.446 1608.2268_8.392 834.5987_8.394 913.7362_8.395
## 2      9237.9250     16053.6450       2900.1672    153612.4200      7233.6950
##   850.5715_8.394 1240.9027_8.395 1647.2081_8.395 1636.2128_8.395
## 2      7632.6410       5042.2656       5742.0024       1433.1611
##   1646.2051_8.395 1241.4043_8.395 870.6699_8.393 823.6064_8.398 943.7468_8.396
## 2       4859.8794       3468.6155      8020.4740      7488.8115     16223.1800
##   1625.2283_8.396 899.7208_8.396 812.6186_8.396 902.5848_8.397 1624.2248_8.397
## 2      28604.9120      6616.0740   1047427.9400     11303.6440      23280.4040
##   829.6782_8.401 1229.9108_8.397 929.7313_8.398 887.6842_8.398 928.5901_8.4
## 2      2905.9430       1786.8480     30229.9940      9576.3630    3768.2612
##   1488.1014_8.407 1579.148_8.401 1556.1645_8.4 812.5816_8.403 1578.1446_8.401
## 2       1599.0760      6521.3916     8926.7280      4463.5210       5768.2610
##   744.5536_8.408 1558.2047_8.408 831.6573_8.408 836.6141_8.438 766.5356_8.411
## 2     67014.3900       5426.7380      6645.1600     67513.0000     17174.8070
##   1559.2145_8.411 1648.2179_8.411 835.6663_8.407 1649.2233_8.415 797.6453_8.401
## 2       4953.9040       4564.8623      3489.7559       4294.2360     18578.6950
##   800.616_8.426 812.5434_8.427 746.6055_8.435 836.6148_8.439 862.6306_8.44
## 2     7890.5930     26353.0020     87903.0400     67513.0000     4183.6226
##   834.5249_8.443 863.7199_8.448 953.7312_8.446 768.587_8.451 858.5977_8.453
## 2      8687.0380      4957.3994      2759.5295    16053.6450     14241.1680
##   838.626_8.451 820.6211_8.471 842.6021_8.473 827.663_8.483 872.6526_8.477
## 2    12044.2730     19120.0120      5486.3965     2012.2316      2843.0916
##   732.589_8.53 822.6349_8.528 816.5872_8.538 911.7203_8.543 1681.1596_8.548
## 2    2401.3503     13368.5080     13415.1080      2542.0745       1467.8942
##   794.6057_8.544 846.6354_8.545 824.616_8.544 627.5345_8.545 904.5911_8.546
## 2     55114.2030      4117.5950     8617.4610     51406.0040    123015.2700
##   887.564_8.547 974.6686_8.55 909.5461_8.55 988.6841_8.551 931.5277_8.561
## 2    59375.2900    16389.7000    76121.2100     12244.3060     18477.8870
##   628.54_8.549 682.5516_8.574 682.5424_8.581 797.6383_8.603 774.5401_8.626
## 2   26479.1230      3619.2190      2558.0005      5545.0244      4538.0350
##   800.6163_8.637 772.6196_8.643 762.6465_8.683 770.6056_8.664 887.7201_8.662
## 2     17393.1390     18083.7420     11254.5070     34141.5270      3043.4792
##   879.715_8.665 762.6007_8.665 762.6003_8.665 792.5873_8.665 784.5827_8.668
## 2     3347.9624     69766.6900     69766.6900      7310.0664     14434.0940
##   783.6251_8.708 760.6411_8.71 761.6436_8.707 603.5342_8.674 885.5458_8.676
## 2     12006.4740   178180.7800     47402.3000     12115.9220     12543.1630
##   880.5906_8.676 881.5938_8.68 863.5639_8.681 863.6636_8.706 798.5401_8.707
## 2     17983.9360     8863.7240     10738.4630      3641.4660     12199.6130
##   776.5588_8.709 864.7169_8.689 782.6223_8.717 759.6382_8.714 781.6192_8.718
## 2     31320.0100      1566.1786     37748.9650    377947.8000     79606.5800
##   913.7359_8.729 818.603_8.731 796.6218_8.731 1592.2329_8.736 1556.2518_8.736
## 2     10978.3260    33741.2660    184381.3600       1825.2245       2584.3364
##   927.7519_8.733 1555.2487_8.735 1581.2641_8.744 1518.2654_8.731 1544.281_8.743
## 2      4942.7180       2985.7680       1573.4025       2188.2922      1780.5391
##   812.6149_8.745 787.6592_8.744 785.6536_8.747 786.6565_8.746 807.6347_8.75
## 2     29369.2540     24286.8890    155052.8800     84624.1950    34807.3670
##   808.6375_8.751 792.551_8.764 770.569_8.759 1096.0441_8.765 1585.2312_8.763
## 2     19958.3960     3824.7146    10167.5220       4770.1170       2009.1298
##   1547.245_8.769 813.6183_8.757 1584.2261_8.767 872.652_8.777 838.6321_8.779
## 2      1813.7694     15552.5370       1495.6147     3547.7883     71823.5000
##   955.7465_8.781 860.6133_8.785 802.5738_8.798 812.6135_8.749 844.6182_8.806
## 2      4056.5420     19085.7480      2034.8574     29369.2540      9798.0690
##   822.6365_8.807 1626.2381_8.815 772.6209_8.839 772.6206_8.839 1560.2277_8.857
## 2     37384.1760       1795.6345     36643.1300     36643.1300       2528.1110
##   895.575_8.852 1610.2416_8.847 1561.2317_8.857 868.5564_8.853 1560.2279_8.857
## 2     9122.6810       1606.6274       2096.7686      8738.1090       2528.1110
##   970.718_8.858 878.5852_8.857 1205.4054_8.863 1193.9116_8.867 1204.9038_8.864
## 2     2072.0452     15522.6260       3425.4846       2413.8950       6387.4320
##   889.7358_8.865 799.6069_8.865 810.5988_8.865 1204.4035_8.865 846.6352_8.854
## 2      6697.9346      6983.0093    162977.5800       4851.0835      3632.3313
##   826.5716_8.868 1599.2099_8.871 788.6177_8.867 875.7207_8.87 919.7465_8.87
## 2      8242.2870       6221.9805    865810.5600     7441.5850    15927.7960
##   1600.2146_8.869 905.7311_8.871 1598.2061_8.871 1576.2251_8.873
## 2       4016.7202     29489.8850       5958.5270      23088.7300
##   1577.2283_8.872 863.6838_8.874 846.6653_8.88 846.6641_8.878  920.75_8.87
## 2      23334.6720      7788.0283     6441.1836      6441.1836    8393.0180
##   862.6249_8.847 798.635_8.894 1562.15_8.919 1541.1702_8.921 1540.1672_8.92
## 2      2585.2780     6228.3520     2620.2407       3824.1492      4577.3970
##   1574.2638_8.923 1573.2611_8.925 1599.2772_8.928 1600.2791_8.929
## 2       6542.3990       6040.3880       5463.1610       5027.1150
##   752.5592_8.969 1540.1695_8.922 634.54_8.941 1424.1384_8.941 835.6566_8.968
## 2    121846.2340       4577.3970   11649.0200       1106.2727     16823.7230
##   807.6349_8.948 1570.2972_8.954 1571.3005_8.952 785.6542_8.953 1618.2939_8.956
## 2    100125.7600       4251.0780       3762.8994    522479.3000       2913.5898
##   1619.2973_8.957 1597.3162_8.956 1226.4736_8.96 1596.3127_8.957 902.7673_8.957
## 2       2636.0408       6456.7925      2384.7297       7183.5527      4785.4880
##   864.6463_8.959 1622.3273_8.96 1623.3314_8.96 1538.2067_8.962 811.6703_8.961
## 2      3853.0010      3328.8547      2920.3967       8198.0080    452180.2000
##   1537.2035_8.961 942.7986_8.961 928.7831_8.964 1563.2185_8.966 1564.2222_8.965
## 2       8158.6310      3112.7622      4864.0244       6779.1787       7188.4610
##   1559.1849_8.966 820.6364_8.966 1560.1891_8.966 1505.1127_8.971 752.5594_8.973
## 2       4831.3480      5543.6120       4781.2480       3282.3630    121846.2340
##   833.6507_8.971 1504.1092_8.971 1585.2008_8.971 1586.2043_8.972 839.6622_8.973
## 2     90670.3750       3353.8713       3973.0388       4660.1130     11185.8020
##   1526.0908_8.976 1527.094_8.977 853.6786_8.975 853.6786_8.974 774.5404_8.982
## 2       2261.3677      2447.8135      6491.6400      6065.3050     33070.9960
##   901.6373_8.994 796.5222_9.007 773.6516_9.025 848.6521_9.028 870.6336_9.039
## 2      5242.9307      6539.7510      5768.3096     10455.5700      3922.0080
##   778.5756_9.056 800.5558_9.058 639.4955_9.064 634.5399_9.061 814.6319_9.087
## 2     21589.3960      9973.2360      9008.7020     17308.4400     45744.3240
##   728.5588_9.081 772.5221_9.085 815.6606_9.084 814.6318_9.087 836.6136_9.086
## 2     36172.3630      4829.6094      6896.4450     43821.0350     12368.0340
##   815.6352_9.088 976.684_9.115 889.5795_9.114 889.5796_9.114 906.6063_9.114
## 2     23855.8630     3964.2112     10294.8530     10294.8530     19006.6900
##   911.5614_9.119 907.6093_9.114 761.6526_9.142 860.6131_9.152 838.6301_9.147
## 2     15033.8630      9790.6840     13240.4760      3334.6848     10393.5520
##   761.6524_9.144 783.6349_9.156 824.6527_9.167 798.6374_9.202 787.6674_9.202
## 2     14064.4000      4326.3877      3069.5030     10325.2540     11397.3890
##   837.6831_9.217 746.5685_9.239 836.6135_9.214 1134.7862_9.281 805.6784_9.291
## 2      2259.9702     23541.2090      5999.7593       2264.9692      3160.8816
##   831.694_9.318 799.6686_9.32 821.6501_9.321 853.6758_9.321 825.6815_9.335
## 2     9682.3840    64151.5080     19314.0740      4403.1426      4570.6104
##   795.6345_9.346 773.6531_9.348 796.6374_9.346 829.6788_9.393 790.5588_9.389
## 2     31471.4530    106267.2400     15768.1130      2430.6433     29043.0450
##   812.5409_9.393 835.6652_9.434 813.6834_9.437 911.5609_9.505 748.6211_9.504
## 2     14324.1460      2862.2705      5873.9604      3171.0100      8226.1540
##   906.6086_9.509 830.6471_9.551 825.6817_9.564 825.682_9.561 825.6778_9.575
## 2      4015.0657      2937.1077      5497.7520     5497.7520      5497.7520
##   799.6692_9.585 821.6503_9.585 889.6375_9.586 824.6537_9.596 865.5795_9.626
## 2    126674.4500     40410.2420      5655.4565      8026.0327      9856.3400
##   882.6062_9.627 605.5497_9.629 887.5614_9.632 610.5401_9.655 874.6674_9.727
## 2     11044.4110     12343.1300     13329.2830     28221.0230      3739.4768
##   946.718_9.767 822.6412_9.776 994.7158_9.773 972.7339_9.773 610.5401_9.784
## 2     4369.6255      3652.0278      3394.0657      6745.5063     26610.0080
##   575.5027_9.782 775.6661_9.814 848.6521_9.817 802.5715_9.823 780.578_9.823
## 2      7375.3936      3828.1284      6023.0610      7545.5100    20116.2090
##   837.6724_9.906 636.5558_9.845 877.6374_9.86 1454.1868_9.88 1428.1716_9.868
## 2     21972.9940     42947.8500     7760.8450      2822.0042       3884.6777
##   790.6308_9.873 836.6696_9.91 809.6507_9.876 825.6241_9.886 1229.9993_9.9
## 2     10953.7200    68698.6640    142217.1200      5103.1763     5663.7266
##   1216.4896_9.892 1596.3093_9.895 822.6567_9.894 904.7835_9.894 787.6701_9.896
## 2       6419.0737       2667.2332      9440.6710      7669.8687    764947.4000
##   918.7987_9.897 1575.3325_9.895 1597.3131_9.894 1216.9911_9.896
## 2      3990.7144       9844.8850       4571.2026       6257.8867
##   1574.3292_9.897 1610.32_9.892 1218.0011_9.9 1205.4975_9.898 1601.3486_9.899
## 2       9586.8050     2037.4537     2942.3462       2605.5000      15603.9630
##   1624.8418_9.901 1612.335_9.9 1600.3452_9.899 1622.3263_9.899 1204.9948_9.897
## 2       1690.1177    3212.6940      17856.8520       6902.9717       2218.2852
##   1623.3297_9.9 1626.36_9.901 1229.4977_9.901 823.1597_9.895 1636.3352_9.901
## 2     6103.9395     7419.6340       5963.9860      9982.1730       1853.3842
##   1218.5051_9.902 813.6866_9.902 1611.8348_9.901 851.64_9.902 1648.3413_9.902
## 2       2684.0940    690934.4400       1809.0951    6419.0205       2774.8425
##   944.8145_9.903 1635.8345_9.901 930.7989_9.903 1627.364_9.902 1231.5117_9.905
## 2      4155.0170       2170.7930      6697.0640      7962.1280       1438.7896
##   1649.3448_9.904 1242.5053_9.906 835.6666_9.91 1637.3363_9.913 1624.3343_9.899
## 2       2797.6785       2477.2144   129321.3500       1596.2676       4526.2295
##   903.6531_9.918 1454.1867_9.904 1455.1905_9.914 811.657_9.886 900.6836_9.96
## 2      7390.5300       2822.0042       2837.3228    22864.7850     3004.9622
##   824.6526_9.963 816.6467_9.969 846.6342_9.978 850.668_9.982 1260.0333_9.997
## 2     22027.6130      9653.9910      6442.0044    21643.2930       1689.8410
##   636.5558_9.995 877.6374_9.988 872.6492_9.995 641.5114_9.997 874.6655_10.042
## 2     56788.6400      7760.8450      6610.9883     25429.3830       4552.4736
##   1285.8106_10.085 1237.8134_10.084 1259.7951_10.087 1263.8289_10.086
## 2        3408.2200        6988.3945        5009.2383        4262.3896
##   848.6549_10.11 839.6989_10.178 802.5716_10.204 893.6243_10.204
## 2      3186.0747       6096.6980       5188.3584       5797.2056
##   903.6531_10.207 780.5795_10.219 835.6664_10.22 1242.5051_10.22
## 2       8449.8540      22486.0720    103869.7700       2082.8200
##   813.6854_10.226 1649.3444_10.225 1626.3599_10.225 1627.3632_10.227
## 2     381365.5300        2159.2188        3409.6960        2932.7860
##   930.7988_10.228 993.7984_10.238 876.6835_10.245 850.6799_10.276
## 2       3466.2046       1542.3507      22272.3380      10435.3720
##   827.6998_10.277 849.6809_10.279 790.6874_10.339 837.6789_10.324
## 2      60154.3360      14998.0840      22034.1110       9877.1330
##   789.6837_10.343 817.7051_10.344 1263.8289_10.349 789.6834_10.343
## 2      40494.2800       6994.6670        5731.1290      40494.2800
##   1136.8021_10.373 784.6654_10.38 810.6812_10.387 827.6999_10.389
## 2        3619.1929     49122.8320      25851.0210      26534.7050
##   1392.1715_10.401 823.6664_10.409 918.7994_10.407 1624.3414_10.412
## 2        3190.6000      62043.8160       4696.2188        2813.8708
##   801.686_10.409 1602.3605_10.41 1603.364_10.411 612.5558_10.456 577.5187_10.46
## 2    692194.6000      11764.9390      12013.2330      77427.2500     32375.0840
##   1251.8293_10.466 576.5712_10.467 1273.811_10.468 638.5716_10.5
## 2        8640.0060       5095.9053       3408.7230   165529.3800
##   827.6999_10.502 828.702_10.497 603.5343_10.505 1061.8543_10.506 974.75_10.504
## 2      25615.8340     15173.9660      34431.8750        3343.0020    11808.7040
##   1281.8397_10.519 620.5974_10.522 602.5849_10.522 646.6126_10.526
## 2        6460.2583      18829.7050       5623.0010      11654.9940
##   798.681_10.534 876.6836_10.537 837.6818_10.567 1631.3955_10.568
## 2     32928.6000      16258.8250      39000.6450       28180.2300
##   1630.3921_10.568 815.7025_10.568 932.8152_10.571 1693.3899_10.581
## 2       28998.0080    1358551.4000      14281.7510        3901.8900
##   1694.3938_10.581 1265.8449_10.582 1266.8482_10.583 878.6996_10.587
## 2        3288.1887       12453.4190        8237.1250      66347.1600
##   852.6843_10.599 812.6971_10.626 634.6121_10.629 794.6868_10.627
## 2      28785.6070      79744.4140      13066.2030       8050.0093
##   829.7155_10.657 604.6026_10.678 622.6132_10.675 648.6287_10.678
## 2      93435.5860      24810.2850      92957.0600      65970.8050
##   630.6177_10.682 750.7353_10.695 766.6703_10.657 636.6288_10.754
## 2      16101.6500      13713.2750       2498.5890      61143.0350
##   618.6182_10.756 1298.1983_10.827 1297.1947_10.828 1301.284_10.831
## 2      18534.6330       14894.1600       15831.9220       9458.5210
##   650.6449_10.83 632.6339_10.83 1300.2808_10.831 1323.2657_10.832
## 2    231979.9800     61316.7150       10455.5960        7166.7656
##   1322.2624_10.832 737.7496_10.83 751.765_10.833 652.6596_10.839
## 2        7450.3690     99716.0400     43819.7660      29194.0640
##   698.6194_10.879 369.3508_10.879 822.6568_10.855 706.612_10.9 367.3357_10.902
## 2       7585.7500       7600.8240       1118.7153     503.6229       2734.6660
##   1312.1372_10.911 536.437_10.971 537.4409_10.971 794.7051_10.958
## 2        1260.3538     15194.1230       6173.0380       1532.0643
##   764.6932_10.985 750.6771_10.977 723.7681_10.986 990.7559_11.04
## 2       3286.6401       4052.7825       6541.2540       503.6229
##   916.7378_11.078 986.8154_11.075 966.7541_11.082 790.6914_11.103
## 2       4055.8555       2141.5740       1308.7278      14667.0980
##   871.6776_11.111 866.723_11.11 897.6938_11.125 1012.8318_11.122
## 2       1971.7200    11646.0570       2771.5180        2667.5410
##   942.7538_11.123 816.7069_11.125 886.7839_11.127 992.7701_11.13
## 2       7002.9956      24550.1150       8966.7450      1220.1979
##   892.7383_11.126 947.7089_11.125 1026.8485_11.128 893.7418_11.129
## 2      13834.8020       1734.7812        1478.2411       9333.2820
##   847.6774_11.153 842.723_11.155 912.7986_11.157 1077.8922_11.168
## 2       4512.6826     35983.4730      12624.6630        3036.9690
##   1052.864_11.17 923.7092_11.174 988.8322_11.176 918.754_11.178 873.694_11.187
## 2      1462.3789       4076.4150       5921.2407     24098.1270      7523.4478
##   873.6937_11.187 1038.8471_11.18 868.7389_11.19 938.8159_11.193 537.4408_11.2
## 2       7523.4478       1416.3619     59879.3550      20733.0400    10269.4940
##   536.437_11.201 973.7251_11.18 899.7091_11.203 964.8324_11.205 978.8481_11.206
## 2     22663.3340      1255.9167       9732.2420      18701.9000       8140.4380
##   894.7548_11.21 895.7581_11.209 949.7248_11.221 792.7074_11.221
## 2     68870.3500      46552.7540       3564.0085      53668.0040
##   1028.8632_11.223 1014.8477_11.222 944.7698_11.221 994.7855_11.222
## 2        2733.2075        5224.7300      21662.3790       1416.0491
##   965.8354_11.205 1064.8634_11.211 823.6785_11.238 818.7234_11.241
## 2      14816.6560        1068.8082      12250.7220     122100.3100
##   888.8006_11.24 979.8509_11.21 880.718_11.248 1651.3848_11.251 966.8463_11.305
## 2     42924.8700      7065.9873     25625.6050        1242.2499      78760.4450
##   1676.3968_11.262 1677.4008_11.264 849.6939_11.262 958.844_11.264
## 2        1796.0378        1726.6401      20410.2400      4713.4663
##   972.8574_11.264 844.7397_11.265 914.817_11.265 928.8321_11.265 984.86_11.28
## 2       2451.9100     204800.8000     66042.9300      29528.2870    4351.0660
##   970.786_11.284 875.7095_11.292 870.7558_11.296 940.8331_11.297
## 2      7937.1480      30375.4340     324633.8400     108197.8600
##   925.7249_11.296 954.8481_11.299 996.8_11.288 990.8484_11.304 920.7705_11.302
## 2      10130.7440      44881.9300    1424.0710      21417.8360      85782.5700
##   967.8518_11.315 1004.8642_11.302 896.7714_11.316 980.8638_11.316
## 2      52803.0660        8446.4310     248298.4800      32281.8000
##   980.8635_11.316 966.8486_11.316 896.7712_11.316 901.7251_11.318
## 2      32281.8000      78760.4450     248298.4800      24962.9080
##   832.7384_11.319 1030.8788_11.336 1016.8631_11.335 946.7855_11.335
## 2      20185.0740        5249.5107       12976.7540      45298.5660
##   951.7402_11.339 863.7092_11.343 858.7545_11.345 972.8_11.345 1678.4082_11.291
## 2       5979.0874       4331.8610      38934.5350    6095.9890        1496.6875
##   897.7743_11.318 794.7232_11.353 864.8008_11.354 1603.3857_11.359
## 2     159883.8400      83499.6600      35786.2300        1324.0599
##   1602.3814_11.358 884.7697_11.358 1629.4012_11.367 1628.3971_11.365
## 2        1567.7642      22661.8000        2671.4620        3046.2450
##   820.7395_11.368 942.849_11.418 927.7399_11.374 1654.4129_11.376
## 2     301490.2000    478812.5600      12192.0160        4399.8080
##   1655.4167_11.377 934.7878_11.376 992.8634_11.384 851.7097_11.384
## 2        4814.8080       3561.1330      46830.5660      37883.4060
##   968.863_11.436 1006.8788_11.385 1680.4284_11.386 922.7862_11.386
## 2    199250.5200       19874.9860        8051.8286     174604.8400
##   1681.4323_11.387 846.7559_11.39 930.848_11.39 916.8333_11.39 872.7731_11.418
## 2        7821.1704    682281.0600   114990.0900    257165.2500    1616521.8000
##   877.7253_11.418 956.8644_11.419 942.8496_11.419 872.7324_11.418 924.8_11.503
## 2      60751.1130     203498.6000     478812.5600       9127.0020  217531.5300
##   898.7872_11.44 982.8793_11.44 968.8643_11.442 971.877_11.497 903.7406_11.457
## 2    669157.7000     92480.0700     199250.5200     17941.2070      37241.9920
##   834.7543_11.459 1018.8786_11.465 948.8005_11.465 1032.894_11.465
## 2      47546.9900       10522.3040      41912.8870       4827.4473
##   953.7558_11.474 974.8146_11.474 860.7704_11.477 865.7253_11.477
## 2       3773.4314       6077.1630     140002.2000      11221.6790
##   936.8015_11.483 891.7406_11.489 886.7858_11.492 929.7558_11.499
## 2       4311.1226       8177.3180      89998.2700      14997.7480
##   994.8787_11.507 1020.8931_11.508 1008.8943_11.508 1034.9093_11.511
## 2      61630.4180        7597.8490       25257.6680        3466.2903
##   1633.4321_11.51 924.8016_11.511 950.8156_11.512 1628.4765_11.515
## 2       2025.4548     228336.0500      27785.3790        1713.1843
##   1658.4443_11.517 892.8326_11.516 1659.4478_11.517 822.755_11.516
## 2        6331.3030     145534.3100        5374.6123    287093.4000
##   1653.489_11.522 1654.4923_11.523 1684.4596_11.522 1685.4637_11.522
## 2       3217.0054        3502.0134        7813.4136       11191.2860
##   951.819_11.515 848.7717_11.527 1679.5046_11.53 1680.5083_11.531
## 2     20169.0200     967240.2000       6822.7740        7704.7427
##   876.6825_11.558 877.7965_11.559 944.8656_11.558 958.8804_11.56
## 2       3346.4666     122302.6640     890314.0000    398300.1600
##   874.7892_11.559 874.7461_11.559 875.5569_11.56 876.854_11.56 820.6961_11.574
## 2    3172819.5000      15439.8930      2351.3545    21837.2010       4369.5210
##   879.7408_11.578 912.8727_11.58 688.6024_11.581 900.803_11.585 970.8801_11.587
## 2      48040.1050      6471.0737      21189.0570   1112878.0000     325268.1000
##   984.8952_11.587 905.7564_11.605 950.8167_11.564 1020.8946_11.578
## 2     149427.5600      44519.5300      27785.3790        7168.3706
##   976.8319_11.618 927.8194_11.647 862.7859_11.623 1034.911_11.594
## 2       3982.1274      77872.8200     222026.7300       2552.7888
##   955.7715_11.624 996.8931_11.636 1010.9091_11.638 888.8014_11.642
## 2       1778.2245      34972.3000       17250.7500     190564.5000
##   1416.1569_11.643 714.6184_11.643 1417.1607_11.643 893.7563_11.645
## 2        1047.7401     102431.1640        1195.3859      23155.9860
##   931.7715_11.646 926.8159_11.647 719.5732_11.65 1022.9086_11.654
## 2      15659.1980     114237.9450      6053.1020        8046.4526
##   1036.925_11.654 1366.1408_11.655 957.7865_11.655 1367.1442_11.655
## 2       4327.8200        2090.9631       4914.2324        1783.4105
##   952.831_11.66 978.8456_11.66 1316.1245_11.664 1317.1277_11.664
## 2    27333.9430      2786.0613        2743.7852        3071.8890
##   664.6011_11.668 1311.1692_11.669 1312.1724_11.668 928.8306_11.753
## 2      34742.4400        3528.9402        2835.1895      67667.6640
##   1689.4913_11.681 1688.4878_11.681 1657.5142_11.686 1683.5318_11.692
## 2        5808.5100        4370.8804         503.6229        5529.8125
##   1684.5352_11.693 960.8944_11.707 946.8797_11.708 876.8035_11.712
## 2        6383.3037     428250.1600     899821.7500    3226125.5000
##   879.8107_11.711 902.8168_11.73 876.7617_11.713 878.587_11.712 972.8948_11.737
## 2     120675.1250    895668.6000      17487.1330      2670.4592     269066.0600
##   902.8182_11.738 986.9104_11.739 1554.3397_11.745 1555.3432_11.744
## 2    1224358.8000     127751.4900        4530.2593        3650.8150
##   1041.9353_11.754 1363.2025_11.753 1364.2059_11.753 690.6188_11.754
## 2        2514.9795       24606.1390       25608.9410     252108.4400
##   673.5905_11.756 1369.1611_11.766 1368.1577_11.767 907.7722_11.767
## 2       8385.0060       16062.5750       14618.8570      60527.9300
##   928.8317_11.769 695.5729_11.772 1012.925_11.773 998.9084_11.775
## 2      67667.6640      14446.9390       9726.1090      22197.7090
##   933.7875_11.787 1339.2017_11.786 1344.1575_11.789 1345.161_11.79
## 2      11010.6820        3396.5154        8898.4680      7879.2250
##   895.7721_11.798 890.8172_11.801 1268.1254_11.807 1295.1453_11.81
## 2      39354.1840     222009.8100        4658.1943      10834.8230
##   1294.1419_11.81 1263.1707_11.815 991.9207_11.815 1289.1866_11.82
## 2      11917.8960        3417.1736       4366.9340      12816.2420
##   1290.1901_11.819 1320.1579_11.832 1321.1613_11.836 369.3517_11.844
## 2       14974.6510       39232.8050       39623.7000     182142.1200
##   1322.1646_11.831 666.619_11.852 370.3549_11.851 1298.1748_11.858
## 2       24688.4920    524568.4400      54212.8670        6319.6870
##   1299.1785_11.856 1017.9359_11.856 1318.2126_11.857 1316.2069_11.859
## 2        6578.4690       25785.3770       28259.0700      165839.2000
##   1315.2035_11.858 1341.2177_11.895 1342.2211_11.896 692.6335_11.899
## 2      159781.9500        7678.6963        8293.4130      46673.7700
##   693.6368_11.899 1532.3557_11.908 1346.1728_11.908 1347.1765_11.909
## 2      23759.4550        3606.4485        6072.4260        6827.9200
##   904.8336_11.91 1558.3711_11.912 988.9264_11.914 974.911_11.915
## 2    709680.4400        4606.4585      99856.6250    219563.4200
##   930.8479_11.919 1000.9255_11.924 1014.9413_11.924 1334.1724_11.947
## 2      77632.0200       26061.6200       12150.8350        1344.1984
##   956.8628_11.946 1308.157_11.948 1322.172_12.024 1350.2033_12.028
## 2       8415.5060       1872.2688       4146.2305        2119.1028
##   1324.1888_12.056 1325.1924_12.055 1299.1762_12.078 370.3549_12.075
## 2       27354.7800       28560.8160       15242.0160      53898.5500
##   1019.9516_12.075 1319.2346_12.075 1303.2104_12.075 1302.2067_12.075
## 2       54721.5500      125490.6000       15517.7540       16427.4260
##   1320.238_12.076 668.6342_12.074 1277.1948_12.078 369.3518_12.077
## 2     123558.3700     166722.5600       12821.5625     186192.3300
##   1294.2217_12.077 1268.2053_12.079 1293.2183_12.078 1326.1956_12.056
## 2       50350.7700        4661.6890       53581.9500       15722.6530
##   1267.2019_12.079 1276.1913_12.079 1250.1754_12.08 642.6107_12.077
## 2        5505.1060       13411.2990       4338.9240      13245.4710
##   1298.1727_12.089 1272.1559_12.098 1028.9572_12.137 963.8345_12.135
## 2       15940.2180        2504.3152        7405.4510       6037.5570
##   984.8939_12.149 1329.2228_12.335 1328.2189_12.336
## 2       4043.2595        2327.4583        2329.1943

# change to results to numeric
data_notame <- data_notame %>%
  mutate_at(-1, as.numeric)

head(data_notame, n = 1)

##                     mz_rt 189.1598_0.513 104.1071_0.537 184.0946_0.54
## 2 x5109_b1_beta_c18pos_65       5393.717       173277.3         51759
##   162.1126_0.545 204.1231_0.589 265.2403_0.591 144.102_0.595 118.0864_0.597
## 2       144957.5       35302.47       25206.29      496116.2       213544.2
##   337.0619_0.611 132.102_0.612 623.1355_0.614 116.0707_0.623 645.1222_0.627
## 2       54898.49      54694.32       11556.97       33922.21       12947.25
##   293.0984_0.628 100.0757_0.629 160.0606_0.629 353.0342_0.629 172.1333_0.62
## 2       669172.9       35459.49       98662.82       26301.62      39135.14
##   120.0808_0.631 166.0861_0.629 258.1105_0.632 310.2012_0.633 188.0707_0.633
## 2       42466.13       49830.12       8127.888       22907.95       38425.38
##   629.1522_0.633 308.1855_0.633 286.2013_0.633 331.0535_0.634 167.0891_0.631
## 2       27773.81       1071.162       37006.47       115179.9       6466.679
##   247.1441_0.636 315.0805_0.637 256.1695_0.637 277.1263_0.644 332.243_0.64
## 2       19227.89       835308.9       503.6229       1397.252      6094.11
##   398.3413_0.639 585.2705_0.643 660.0815_0.643 265.1183_0.643 272.1651_0.645
## 2       503.6229         106325       6281.265        25677.9       1143.596
##   235.0925_0.646 152.0706_0.642 682.0633_0.646 207.1109_0.641 287.1003_0.648
## 2       21110.93       503.6229       4690.715       503.6229       14763.28
##   314.2326_0.654 464.1914_0.656 293.1101_0.657 338.065_0.618 377.2467_0.663
## 2       98041.81       2922.865       13778.01       9382.85       14033.57
##   328.2481_0.675 634.4366_0.676 316.2483_0.679 316.2608_0.679 340.2481_0.684
## 2       18997.65       3395.837       172184.2       10606.57       8409.402
##   648.4519_0.687 692.4782_0.686 736.5043_0.684 533.3253_0.69 706.4934_0.689
## 2        5077.35       4088.035       2706.916      503.6229       3497.064
##   330.2637_0.689 515.3654_0.688 588.4113_0.688 368.2792_0.689 750.52_0.687
## 2       9743.125        3182.81       1957.799       10114.12     2877.078
##   342.2638_0.69 508.3578_0.689 346.0093_0.691 240.1585_0.692 643.38_0.69
## 2      34116.93       3075.123       25780.42       503.6229    1043.223
##   356.2794_0.691 363.2166_0.691 254.1387_0.695 344.2792_0.693 205.1221_0.621
## 2       5844.374       7206.663       503.6229       7690.018       5093.727
##   259.0944_0.697 616.1763_0.702 425.1649_0.679 423.1686_0.697 429.24_0.73
## 2       503.6229       4483.476       503.6229       503.6229    1073.373
##   346.0093_0.919 616.1763_0.902 383.1533_0.931 344.2793_0.927 466.3162_1.004
## 2        22806.4       2304.904       503.6229        8477.92       503.6229
##   167.0854_1.05 308.1256_1.117 286.1437_1.13 373.2483_1.147 353.079_1.243
## 2      503.6229       1220.951      9559.344        2644.51      503.6229
##   337.1048_1.268 315.1224_1.279 585.2701_1.361 450.3212_1.383 426.3213_1.384
## 2       1094.075       503.6229       16384.21       4375.302       1517.117
##   413.2797_1.415 399.2641_1.416 312.1597_1.413 463.2323_1.708 531.2196_1.712
## 2       1245.323       1691.521       1938.883       1366.684       1879.289
##   526.2644_1.714 424.3418_1.822 163.0753_1.983 365.1356_1.988 343.1537_2.002
## 2       2830.343       12478.95        1823.27        1293.21       1224.583
##   430.2589_1.992 400.3418_2.122 542.3237_2.234 426.3575_2.274 518.3238_2.281
## 2       503.6229       20248.24       15779.09        25978.1        10080.8
##   468.3082_2.293 516.3057_2.488 494.3238_2.494 590.3212_2.604 568.3394_2.605
## 2       36834.36       6834.443        41801.2        7841.76       42264.55
##   445.2944_2.63 566.3214_2.69 536.2833_2.749 544.34_2.697 482.3238_2.701
## 2      2599.151       56812.7       2144.942     368163.5       43307.03
##   1087.6686_2.703 1085.6532_2.71 1063.67_2.714 1001.6557_2.715 526.2925_2.719
## 2        8532.202        11860.9      45158.13        5241.096       19726.09
##   1128.671_2.722 1045.6241_2.726 578.3923_2.734 522.3458_2.735 1039.6724_2.735
## 2       3874.508        4286.007       8663.166       104104.3        141075.4
##   1050.6573_2.737 1580.9848_2.737 637.4547_2.737 520.3416_2.737 854.4963_2.738
## 2         3425.48        2591.293        9267.76        2110536       1008.148
##   1061.6537_2.738 558.2953_2.739 801.9871_2.739 1051.1585_2.738 802.4888_2.74
## 2        37508.34       25146.85       6660.126        4586.067      5628.042
##   1077.6264_2.732 542.3219_2.742 1321.82_2.739 428.3732_2.759 603.2923_2.783
## 2        2352.143       216674.8      1340.773       22848.51       3954.632
##   524.2743_2.81 502.2925_2.81 570.3547_2.836 500.2744_2.854 478.2926_2.855
## 2       9483.88      45643.72       18142.38       12592.04       59690.58
##   508.3393_2.879 568.337_2.996 546.3554_2.996 1041.6857_3.056 1041.6842_3.059
## 2       8472.406      19313.49       96251.14         7538.01         7538.01
##   518.322_3.062 534.2952_3.067 766.4889_3.074 765.9874_3.073 554.3925_3.077
## 2      261189.1       29635.33       12595.91       14793.69       14094.73
##   515.313_3.077 762.9789_3.079 1002.658_3.079 1029.627_3.079 1003.1594_3.079
## 2      6508.718        3456.11       12248.12       7750.699        12764.03
##   1018.1281_3.082 1250.8236_3.081 1013.6543_3.08 1011.1465_3.08 1258.8122_3.08
## 2        3244.351        3285.566       134102.8       4763.765       4368.292
##   1010.6446_3.081 1508.9859_3.081 1261.821_3.081 1018.6298_3.081 496.6809_3.081
## 2        3859.514        23519.38       12869.56        4046.246       4457.254
##   1509.9892_3.081 583.4441_3.082 597.4596_3.082 498.346_3.082 627.4702_3.082
## 2        21969.56          12773       8819.312      195381.3       10046.37
##   1487.0039_3.082 991.6743_3.082 496.342_3.082 613.4547_3.084 519.3249_3.064
## 2        15511.97       879353.2       4294420       17320.18       74408.87
##   1026.6606_3.098 1339.8437_3.087 1017.6873_3.095 1039.6688_3.106
## 2        3632.041        503.6229        57205.26        12549.96
##   844.5121_3.103 454.2925_3.202 560.3108_3.237 522.3564_3.24 639.47_3.238
## 2       1817.037       27750.66       17206.49       1083036     5109.145
##   1043.703_3.238 544.3374_3.238 1065.6846_3.239 805.0108_3.238 572.3709_3.254
## 2       42585.41       173100.4        13565.86       3742.731       11761.46
##   502.2902_3.355 480.3082_3.357 480.3446_3.381 482.3601_3.403 504.3422_3.406
## 2       12590.91       50793.16       30530.71       30982.69       6784.817
##   548.3709_3.408 510.3551_3.445 438.2976_3.525 508.3757_3.535 808.0344_3.778
## 2       13561.27       46895.31       13126.19       19657.02       9954.235
##   582.4238_3.778 808.536_3.778 1059.2224_3.779 1069.7165_3.778 1571.0972_3.779
## 2       6841.065      8679.472        2830.685        38131.31        1797.102
##   524.373_3.779 562.3265_3.778 1593.0791_3.779 1047.7355_3.778 1331.8987_3.778
## 2       2038640       20471.24         3281.99        137818.2        2004.737
##   611.4752_3.779 641.486_3.779 526.3772_3.778 1012.7169_3.78 1594.0825_3.778
## 2       7494.673      14114.69       105510.5        3057.31        3136.277
##   546.3534_3.779 614.3398_3.784 550.3862_3.859 482.3239_3.884 504.3061_3.884
## 2       276992.4       11940.78       14665.81       47507.31       17674.91
##   585.2706_3.939 1191.515_3.94 607.2524_3.94 299.139_3.94 686.3908_3.94
## 2       146034.2      16599.96       52378.4     24249.07      6995.175
##   672.3751_3.941 1169.5332_3.94 1213.4964_3.94 629.2342_3.942 508.3754_4.012
## 2       18887.17       25512.06       6557.705       15115.13       6429.108
##   510.3914_4.038 538.3859_4.051 664.5997_4.075 466.3289_4.123 541.3858_4.202
## 2       14236.21       5810.855       503.6229       17835.53       2574.608
##   561.4122_4.23 521.4196_4.236 539.4303_4.244 563.4268_4.262 317.2685_4.264
## 2      23843.74       33456.54       10956.25       12931.34        7061.09
##   552.4018_4.301 593.4768_4.312 563.4273_4.415 619.4811_4.462 798.5661_4.516
## 2       10377.73       5784.829          18468       3092.176       503.6229
##   593.4757_4.54 712.5953_4.577 569.4277_4.639 568.4266_4.64 543.4017_4.622
## 2      4907.904       1515.657        5953.48      12313.46       4421.932
##   617.4748_4.643 593.4762_4.644 595.4929_4.645 577.4817_4.655 515.3703_4.774
## 2       41282.35       5630.025       18788.27       6552.479       1753.653
##   774.5634_4.81 822.5634_4.827 596.4154_4.906 620.4935_4.951 317.2685_4.946
## 2      1473.547       503.6229        2267.32       8821.808        13718.4
##   641.4722_4.95 619.4903_4.953 647.5118_4.967 669.4937_4.965 800.5792_5.005
## 2      4980.564       20838.72       16498.73       3707.525         1091.9
##   798.5655_5.067 695.5093_5.094 673.5278_5.092 669.5575_5.14 610.431_5.16
## 2       1007.246       8457.689       31376.89       2030.23     4867.011
##   597.5086_5.242 822.563_5.243 798.564_5.251 592.4686_5.28 683.5094_5.286
## 2       4058.869      503.6229      503.6229      4339.037       3237.377
##   661.5282_5.288 719.5696_5.337 802.5954_5.397 752.5219_5.513 608.4637_5.649
## 2       11441.75       3824.059       503.6229       2898.786       4542.179
##   1372.06_5.66 834.5932_5.658 765.5124_5.66 1350.0784_5.66 697.5255_5.661
## 2     1504.125       7477.324      10079.44       2835.277       106392.8
##   675.5442_5.66 713.5011_5.661 755.4833_5.662 1035.2944_5.66 834.5933_5.658
## 2      386197.2       3942.706       6335.622       2901.371       8029.474
##   754.5378_5.732 678.5059_5.698 826.5945_5.722 828.5522_5.728 860.609_5.81
## 2       3039.894       5521.155       503.6229       1888.697     6816.476
##   791.5279_5.817 1424.0918_5.818 739.5144_5.819 723.5413_5.819 1074.3179_5.819
## 2       10553.44        3385.897       5583.948       144145.3        3523.327
##   1402.1101_5.819 1403.1135_5.82 1425.0951_5.82 701.5604_5.818 1073.8159_5.82
## 2        4824.275       5107.778       2573.156       599996.9       2832.278
##   1227.7565_5.834 804.5524_5.837 826.5347_5.837 1249.7383_5.838 778.5376_5.842
## 2        12203.02          12628        3870.69        8835.819       12066.24
##   800.5194_5.843 614.3823_5.849 780.5532_5.895 781.5567_5.896 894.5999_5.902
## 2        2907.38       3563.692       11540.62       6917.907       503.6229
##   796.5458_5.943 774.5637_5.944 518.4564_5.944 532.4721_5.945 699.5412_5.971
## 2       3604.923       13888.01       11549.34       7264.864       3503.865
##   677.5586_5.973 826.5348_5.993 677.5582_5.973 826.594_5.988 776.5195_6.001
## 2       13189.33       2856.227       13189.33      503.6229       11136.42
##   754.5373_6.002 1378.109_6.06 689.5597_6.062 711.5408_6.063 870.596_6.054
## 2       40443.72      1011.074       200887.3       50749.24      1772.125
##   1460.0658_6.066 1419.0884_6.065 721.5835_6.066 749.5557_6.066 727.5745_6.067
## 2        503.6229         1109.91       9074.263       5750.457       19276.98
##   847.6527_6.068 721.5846_6.067 730.5383_6.068 752.5201_6.07 768.4931_6.068
## 2       2905.475       9074.263       114104.4      27510.76       1770.284
##   854.5686_6.074 719.5692_6.151 741.551_6.153 1024.6773_6.157 1046.6591_6.159
## 2        6152.89        15576.9      4452.868        10911.22        6503.981
##   836.4936_6.219 846.5226_6.22 873.6685_6.266 778.5354_6.223 756.5541_6.24
## 2       4037.495      4128.233       2879.385       25760.33      110201.8
##   852.5506_6.226 830.568_6.228 852.5508_6.228 715.5745_6.252 814.535_6.26
## 2       5595.419      18959.27       5595.419        15226.4     3462.799
##   792.5531_6.261 1282.9156_6.311 860.4936_6.313 870.5225_6.318 1192.8093_6.322
## 2       12696.33        1923.939        6466.28       9003.215        2011.703
##   887.5125_6.318 803.037_6.321 855.6216_6.322 838.606_6.327 802.5357_6.323
## 2       6353.087      3670.396       3051.434      1869.002       91769.53
##   867.6575_6.328 897.6684_6.323 1583.0843_6.325 1582.0806_6.324 818.5091_6.325
## 2       2372.927       11467.24        1582.399        1568.461       4040.898
##   1332.9345_6.33 1181.3124_6.332 780.5549_6.325 1560.0983_6.326 1572.0922_6.339
## 2       503.6229        1025.953       368834.2        4451.918        503.6229
##   1561.1021_6.326 911.6839_6.326 1083.7892_6.33 791.5397_6.411 1536.0989_6.339
## 2         4407.13       6154.823        2970.02        6074.74        2921.405
##   1180.809_6.327 1586.1131_6.353 1537.1023_6.339 815.5406_6.313 1558.081_6.341
## 2       1127.522        3110.242        2659.378       3513.512       503.6229
##   856.5843_6.351 1584.092_6.345 808.5757_6.369 1563.1161_6.369 806.5698_6.37
## 2       4378.549       1198.424       29549.92        2475.094      195438.1
##   937.6995_6.371 807.5729_6.37 1612.1288_6.374 1613.1329_6.372 1562.1109_6.352
## 2       2721.302      102868.9        1763.787        1275.695        3185.352
##   896.5379_6.373 828.5511_6.373 756.5542_6.373 923.684_6.374 1513.1028_6.383
## 2       4877.083       45829.29       125456.4       6563.71        1131.322
##   778.5355_6.376 873.6684_6.375 1512.0991_6.376 887.6837_6.371 783.5732_6.445
## 2       29409.03       4166.919        503.6229       1988.407       331912.1
##   691.5742_6.403 1586.1128_6.4 862.5091_6.42 1587.1171_6.406 790.5352_6.424
## 2       3770.091      4523.083      5067.804        4852.676       10887.09
##   872.5379_6.428 804.5511_6.435 768.5536_6.434 857.6371_6.438 1195.8324_6.435
## 2       8783.236       111106.5        55822.8       5338.684        2865.332
##   820.5246_6.44 899.684_6.44 869.6734_6.444 1551.1176_6.447 765.0535_6.446
## 2      6667.659     17594.67       3180.421        3467.197       6213.062
##   913.6995_6.445 1155.8333_6.446 1156.3359_6.446 1588.1268_6.447
## 2        8412.68        3080.686        4607.505        6853.002
##   1588.1294_6.449 1550.1138_6.449 782.5712_6.447 1536.6212_6.45 840.6219_6.452
## 2        6853.002        4199.678         678390       1190.592       6660.055
##   884.6064_6.452 1589.1333_6.45 1538.1147_6.455 1536.1192_6.444 1564.1312_6.455
## 2        9413.58        5112.16        5550.979        1409.711        19175.45
##   1565.1348_6.456 1539.1179_6.455 1496.1206_6.463 1496.6252_6.461
## 2         18705.8         4691.44        2144.165        2699.307
##   1116.8381_6.462 1497.1271_6.467 1508.122_6.465 1507.1178_6.465
## 2        8629.759        2977.185       10895.97        13234.06
##   1472.1218_6.472 862.6247_6.471 1468.623_6.472 1468.1206_6.471 1471.118_6.473
## 2        3205.907       29764.36       5167.526        5868.002       5601.302
##   1509.1328_6.475 1457.6299_6.479 1486.1407_6.477 1457.1286_6.478
## 2        11411.89        4680.134        61512.78        5138.557
##   1485.1374_6.478 1527.117_6.481 1565.1864_6.477 1526.1136_6.481 1510.1375_6.48
## 2        60516.32       6122.256        4547.977        6177.338       9231.854
##   1483.1167_6.498 704.4038_6.503 704.3938_6.501 704.5181_6.5 706.5843_6.5
## 2        3701.038        2979.16       3859.773     5754.961     62212.01
##   703.8986_6.501 703.9516_6.503 705.6376_6.502 703.5779_6.502 704.5113_6.501
## 2       4795.362       4313.908       12838.21        3894128       4318.408
##   1469.1118_6.499 1406.1439_6.504 834.7053_6.506 1448.1242_6.506
## 2        6197.371        201903.3       8837.926        23742.54
##   1418.1346_6.507 1447.1208_6.507 820.6896_6.508 1417.6332_6.508 1065.848_6.509
## 2        11429.71        28251.12       14094.65        11612.35        5100.89
##   1066.3498_6.509 1469.1116_6.5 744.5538_6.51 1459.1207_6.512 1419.1345_6.509
## 2         7944.83      6197.371      130015.8        21799.07         4790.94
##   1428.1239_6.513 1429.1272_6.513 1460.1239_6.512 1489.1055_6.52
## 2        38435.32        39882.62         19428.6       3819.562
##   1500.0993_6.518 1512.1013_6.516 758.5603_6.497 1444.1182_6.522
## 2         2863.62         1807.76       16470.03        2425.514
##   1451.0918_6.532 1077.3414_6.528 1411.1131_6.529 757.5569_6.495 1481.1047_6.53
## 2        1944.388        19003.99        4645.933       54360.38       4764.314
##   1482.1063_6.531 1479.1431_6.532 1410.1118_6.532 741.5301_6.535
## 2        3807.777          1477.4        9581.128        13782.2
##   1463.0927_6.535 1413.0725_6.543 1443.1241_6.536 1431.0932_6.55 725.5571_6.544
## 2        1717.434         1060.23        503.6229       2014.533       260720.5
##   706.5381_6.548 1409.1106_6.553 1187.9262_6.553 776.579_6.552 823.6523_6.559
## 2       93600.58        6711.315        4113.951      13343.06       3093.488
##   728.5196_6.564 794.5671_6.58 762.541_6.576 445.3676_6.577 795.5713_6.591
## 2       16527.31       9425.65      1313.938       109687.9       4672.062
##   738.5064_6.603 1175.7015_6.637 1153.7198_6.64 822.6001_6.635 770.5692_6.675
## 2       2870.319        6597.094       11591.68       503.6229       20639.82
##   886.5092_6.697 1576.1291_6.701 751.5715_6.701 1192.8412_6.709 807.3448_6.714
## 2       6770.491        2247.034       44493.56        4918.515       2046.742
##   1193.3431_6.71 1232.3347_6.716 1557.1328_6.71 1585.1303_6.711 1558.1357_6.713
## 2       5770.275        7525.565       4170.914        2856.057        3915.788
##   818.0604_6.711 1458.1721_6.714 1585.6318_6.711 729.5912_6.714 1635.1157_6.712
## 2       4732.001        2063.349        2523.966       306413.6        17849.02
##   1634.1123_6.713 923.6842_6.713 1536.1554_6.715 1535.1521_6.713 806.5725_6.713
## 2        15352.74        32366.5        17665.03        17672.51        1990779
##   896.5381_6.715 1586.1251_6.718 1597.1201_6.716 893.6733_6.713 1623.6198_6.714
## 2       12253.86        2667.938        2787.466       6461.125        3466.673
##   1596.1232_6.714 808.6366_6.716 937.6999_6.715 1612.1323_6.715 907.689_6.715
## 2        2560.059       11384.07       19629.97        77630.02      4811.047
##   1624.1217_6.715 1613.1357_6.716 1596.6247_6.715 1231.8327_6.718
## 2        5639.593        80207.57        2719.564        8712.688
##   1540.1229_6.721 1597.613_6.72 1539.1194_6.72 1538.1166_6.72 829.5547_6.726
## 2        18157.97      3647.161        30808.2       31225.44       112573.4
##   1462.1391_6.719 806.973_6.716 1624.6226_6.715 1461.136_6.721 1598.1117_6.724
## 2        3168.688      1876.863        3109.778       3381.061        3605.303
##   844.5247_6.723 881.6374_6.724 1465.1023_6.726 1194.3346_6.723 1561.1011_6.726
## 2       12924.33       8552.931        3474.738        4337.534         5940.24
##   732.5542_6.723 1560.0999_6.725 828.5517_6.726 1464.0995_6.726 863.6839_6.733
## 2       276055.4        6536.032       217082.3        3964.644       3554.819
##   1486.0818_6.746 830.5605_6.803 1588.1294_6.745 819.6577_6.728 810.5875_6.727
## 2        1056.584       36147.89         6560.44       2858.344       16855.02
##   864.6223_6.744 791.5433_6.743 849.6682_6.746 1570.1174_6.785 1571.1203_6.766
## 2       9567.526       4449.957       8039.013        2437.093        1673.151
##   1232.8362_6.721 1564.133_6.867 752.574_6.803 1639.1441_6.788 764.5588_6.812
## 2        6162.027        1592.23      22218.88        1738.531       19950.18
##   1389.0311_6.813 446.3707_6.814 829.5548_6.746 848.6162_6.816 1547.1209_6.83
## 2        2245.297       15048.38       112573.4       1549.012       1851.035
##   445.3675_6.816 786.5403_6.817 1610.1103_6.827 816.5509_6.821 794.5677_6.822
## 2       44383.05       13006.08        2360.677       12866.78       23135.77
##   770.5691_6.825 756.5471_6.823 752.5739_6.808 754.5356_6.825 830.5623_6.824
## 2       45051.02       13398.25       21862.45       82551.06       44440.26
##   775.6384_6.825 1638.1373_6.816 751.5717_6.812 889.5277_6.826 792.5512_6.827
## 2        9095.53        2424.075       41401.25       12069.74       31182.04
##   1189.3305_6.84 862.5095_6.827 429.3727_6.826 812.5558_6.826 740.5662_6.833
## 2       2290.845       12648.29       29734.88       9023.723       11398.99
##   717.5903_6.828 827.6071_6.849 718.5932_6.828 725.5564_6.829 788.554_6.828
## 2       31804.75       6890.672       18248.29       6774.083       7176.18
##   824.5758_6.83 1233.3393_6.794 703.5745_6.822 1546.1205_6.851 806.6235_6.835
## 2      5052.685        2624.717       17905.92        2267.558       8408.094
##   1664.1589_6.841 1341.0272_6.83 1536.1083_6.846 802.5963_6.831 790.5738_6.832
## 2        503.6229       1807.081        4108.062       5939.143       14675.88
##   791.5771_6.835 870.5402_6.834 854.5671_6.833 762.5403_6.835 742.5356_6.834
## 2       10014.84       5618.031       95410.94       5182.002       11383.88
##   1363.015_6.835 742.5728_6.833 1365.0287_6.836 800.5726_6.827 793.556_6.829
## 2       4216.765       6285.115        6167.119       2866.086      19775.47
##   1339.0138_6.829 820.5836_6.837 755.6054_6.838 720.5531_6.843 949.6995_6.841
## 2        1964.223       3550.497       12663.03          20646       3175.722
##   1207.3311_6.841 832.5855_6.839 740.5626_6.841 780.5519_6.84 963.7151_6.844
## 2        3592.237       215492.3       11398.99      268613.8       2147.896
##   805.617_6.84 1170.3294_6.844 740.5609_6.842 727.5724_6.844 1169.3422_6.843
## 2     23781.82        5499.694       11398.99       71935.27        2549.335
##   1208.3368_6.844 1221.342_6.847 796.5247_6.843 1208.8392_6.844 804.553_6.847
## 2        8168.149       8990.758       13196.66        8508.403       1490167
##   1220.8404_6.847 1171.3299_6.847 822.528_6.849 1490.1152_6.846 706.5938_6.852
## 2           11420        6874.279      12749.64        2711.425       67258.38
##   1637.1286_6.848 820.5251_6.85 1537.126_6.857 1550.1223_6.858 705.5909_6.851
## 2        4997.227      56154.94       5891.694        4848.113       164496.8
##   1611.1156_6.852 1144.8407_6.848 1599.1159_6.853 1511.145_6.854 1601.625_6.853
## 2        4880.624        2717.055        4693.414       3669.107       4794.322
##   1549.6231_6.854 1157.3434_6.852 1145.3428_6.85 1171.8322_6.85 1510.1351_6.854
## 2        3179.157        6450.307       2907.835       9182.755         3332.92
##   1599.6188_6.853 766.5727_6.851 1183.8334_6.853 1509.1326_6.853
## 2        5672.694       17519.13         34739.1        3473.422
##   1600.1221_6.854 1184.336_6.855 1611.6196_6.855 1612.6219_6.855
## 2        7925.387       26521.11         4443.62        3518.422
##   1600.6232_6.854 1580.1025_6.858 1536.6252_6.856 1601.125_6.853
## 2        6881.685        3157.408        3068.867       7206.609
##   1536.1177_6.855 1195.8336_6.857 833.6399_6.878 1157.8451_6.853
## 2        4108.062        68607.18       5561.771         7063.84
##   1573.1314_6.859 1548.6249_6.858 1523.1232_6.86 1537.1276_6.858
## 2        2732.128         4955.48       2464.699        5891.694
##   1589.1303_6.863 826.6051_6.862 1562.1129_6.862 1574.6139_6.862
## 2        19960.34          12937        22721.02        20510.01
##   1581.0989_6.863 1553.1294_6.871 1575.1173_6.863 1587.1165_6.864
## 2        2159.307        5932.734        29707.46        73110.51
##   1586.1131_6.864 1487.1515_6.871 1563.1166_6.865 1575.6183_6.866
## 2        56394.65        11787.87        28489.42        38242.09
##   1592.5935_6.867 1563.6184_6.864 1514.1154_6.867 1576.1212_6.867
## 2        3451.812        17199.99        11699.04        35512.44
##   1548.1292_6.867 1572.126_6.868 1580.5988_6.86 840.6221_6.868 1616.1518_6.868
## 2        6371.311       2966.945       2407.343        19593.6        13179.28
##   1615.1494_6.868 784.5042_6.876 785.0201_6.879 1593.594_6.87 1594.0976_6.869
## 2        24269.12       6912.227       7153.885      3847.872        3865.208
##   1502.1143_6.871 1614.1454_6.868 784.7847_6.875 1564.621_6.871 857.6374_6.871
## 2         4114.43        23931.69       22815.42       15694.01       15725.66
##   784.4182_6.876 784.3687_6.876 783.1226_6.878 783.4174_6.877 1593.0936_6.87
## 2       5728.139       5230.898       5396.507       9845.552        3322.19
##   1515.1185_6.869 913.7001_6.872 1487.1511_6.872 783.1504_6.876 785.5792_6.874
## 2        12058.39       55061.86        11787.87       5308.917       142187.2
##   899.6847_6.873 783.3656_6.875 784.779_6.877 782.9105_6.881 784.6308_6.874
## 2       77207.88       9307.482      22815.42        9752.22          31362
##   783.332_6.875 783.4393_6.874 783.3243_6.874 783.2199_6.876 784.3942_6.875
## 2      7112.816       9775.993       9051.518       6609.113       5407.648
##   1577.1237_6.867 784.4291_6.877 783.1601_6.876 785.0064_6.878 783.1327_6.878
## 2        17270.28       6519.669       5370.077       7101.736       5553.358
##   782.5732_6.875 783.2011_6.878 1488.1552_6.873 783.5024_6.875 783.2594_6.875
## 2        5118052       5738.108        10344.36        16438.4       8841.006
##   783.2926_6.876 783.431_6.876 783.2519_6.875 783.2153_6.875 783.3975_6.877
## 2       8879.737      11415.32       8841.006       6609.113       9822.561
##   783.3843_6.877 784.4472_6.876 794.0606_6.876 783.4057_6.877 785.0389_6.88
## 2       7887.037       7024.986       10706.02       9822.561      7854.313
##   785.049_6.877 783.2313_6.877 784.4911_6.877 786.5814_6.876 782.9653_6.878
## 2      6642.376       7012.737       7622.044        29706.6       6474.723
##   783.067_6.877 784.5306_6.877 783.1442_6.879 783.0991_6.879 784.3802_6.879
## 2      5600.567       7788.103       5385.042        4890.15       5426.719
##   784.3306_6.878 782.9942_6.879 783.3522_6.877 783.1872_6.878 784.3478_6.878
## 2       4889.519       5416.948         7914.3       6745.338       6518.784
##   783.3414_6.876 1562.1131_6.863 784.4086_6.876 782.5279_6.881 784.3757_6.877
## 2       8348.811        22721.02       5975.541       14215.55       5426.719
##   1590.1432_6.882 1549.1336_6.872 784.9793_6.877 1548.1302_6.87 1566.2163_6.888
## 2        22002.96          6424.1       8130.741       6655.745        22692.19
##   1564.1347_6.888 897.7049_6.893 1569.1509_6.94 883.6894_6.894 869.6738_6.894
## 2        850852.2       6647.499       23776.19       17272.91       20351.44
##   1525.1338_6.906 1565.1377_6.892 784.6324_6.878 1592.1507_6.918
## 2        2977.818        861451.7          31362        12488.21
##   1552.1254_6.917 1463.1508_6.915 1172.8405_6.921 1172.3364_6.884
## 2         12711.5        2801.778        10840.85        9370.101
##   1551.1225_6.925 1550.14_6.908 782.0605_6.928 1591.1484_6.916 1551.6195_6.93
## 2        9737.188      4848.113       8147.896        22023.91        11321.2
##   1540.1336_6.931 1542.2142_6.931 1541.137_6.931 1541.2129_6.933
## 2        486983.7        11582.83       465805.6        18545.88
##   1524.1325_6.944 1160.8402_6.939 1539.62_6.944 1160.3363_6.993 890.7034_7.007
## 2          3996.6         12344.9      13607.31        11612.71       24366.79
##   846.6771_7.001 876.6878_7.013 759.4494_6.981 759.2363_6.979 760.4788_6.981
## 2       14237.49       26663.73       13573.45       9096.314       8931.823
##   759.4107_6.98 873.7049_6.978 760.3659_6.98 759.1825_6.981 761.2418_6.982
## 2      12453.77       8745.555      7240.025       8073.894       7632.623
##   761.5791_6.979 758.6931_6.98 760.5104_6.985 759.3448_6.983 758.5734_6.982
## 2       159828.2      151150.5       10110.68       12389.06        5924760
##   759.3553_6.982 759.5018_6.981 759.317_6.981 759.3092_6.981 760.4052_6.983
## 2       11545.61       19481.56      11961.75       11055.11       7653.589
##   759.3828_6.982 760.4925_6.98 759.4259_6.982 759.3339_6.982 1516.1361_6.982
## 2       13261.56      9171.551       12957.39       12457.11         1410985
##   759.2062_6.983 759.391_6.982 758.9593_6.983 759.2797_6.981 760.6287_6.982
## 2       9527.567      13356.74        7871.16       10960.33        37455.3
##   760.421_6.983 759.2558_6.98 760.5281_6.984 761.2551_6.982 1517.1828_6.983
## 2      8018.087      10437.23       8964.443        7059.67         70068.8
##   759.2209_6.98 760.3904_6.983 759.1347_6.985 758.9871_6.983 759.2864_6.986
## 2      8099.955       7451.463       7856.265       7519.873       10960.33
##   759.1729_6.983 759.1221_6.983 759.0903_6.983 759.243_6.983 760.4378_6.981
## 2        8494.09        8485.67       9391.726       9508.17       7236.265
##   859.6895_6.984 761.0316_6.983 759.1094_6.983 760.5329_6.983 759.058_6.985
## 2       23681.34        9723.29        7969.41       9791.593      7774.229
##   758.5284_6.987 759.4041_6.982 760.9991_6.984 760.3462_6.982 760.2985_6.984
## 2       16056.29       13356.74       10946.29        6063.82       5087.155
##   759.2652_6.984 759.0221_6.985 760.7903_6.983 760.2926_6.982 759.1449_6.983
## 2       10437.23       7333.553       25374.46       5774.973       6841.049
##   760.3968_6.983 760.3759_6.985 760.6318_6.982 1518.2168_6.99 748.5833_7.03
## 2       7653.589       7195.236       35895.73       27325.66       1124.02
##   761.0061_6.983 1528.1243_7.002 1148.8422_7.002 1527.6219_7.002 760.5406_6.984
## 2       10946.29        18321.87        18726.27        16791.13       8467.766
##   1475.1518_7.004 845.6738_7.007 1476.1552_7.005 889.7001_7.009 769.5595_7.011
## 2        7617.863       23587.66        8129.001        40159.9       14592.27
##   770.0615_7.009 1564.1269_7.052 718.5934_7.015 717.5903_7.018 875.6845_7.017
## 2       14226.67        9514.498       18629.59       38487.53       49967.14
##   1523.0877_7.024 1478.1161_7.023 1522.0846_7.024 1479.1185_7.024
## 2        11077.44         5792.93        12143.67        4028.012
##   1568.1537_7.025 1567.1512_7.025 1538.1135_7.026 1539.117_7.026
## 2        73506.92        138715.3        38153.05       43548.25
##   1566.1476_7.026 1553.1308_7.035 1159.3314_7.035 764.5222_7.036 720.5532_7.039
## 2        146112.4        8087.822         9756.19       24172.59       14088.04
##   1550.1462_7.021 1552.6273_7.034 1159.8332_7.037 1544.0675_7.037
## 2        3413.481        8553.492        15630.45        4475.843
##   1545.073_7.035 833.637_7.039 1173.8476_7.044 781.62_7.043 1572.101_7.045
## 2       3244.006      7982.129        6445.105     5688.709        2317.23
##   780.5518_7.046 1173.3456_7.052 811.5943_7.054 1184.8408_7.054 809.5892_7.057
## 2       183253.7        5460.866        19779.6        8766.134       291041.8
##   1617.1644_7.055 808.586_7.055 786.504_7.061 1616.1606_7.056 1578.1434_7.057
## 2        11359.09      588403.1      3929.747        11933.23        6161.712
##   792.5893_7.058 858.5992_7.06 939.7152_7.063 848.5388_7.066 1185.344_7.069
## 2        15391.7      2558.981        7418.66       9512.555       6107.932
##   925.6997_7.067 1172.8404_7.074 830.5667_7.072 1197.3456_7.08 1578.1423_7.058
## 2       11909.18        3174.133       70597.19       3549.967        6161.712
##   1593.1638_7.133 1577.1364_7.091 1564.1272_7.069 794.0604_7.102 793.5586_7.102
## 2        20253.26        2604.576        9514.498       7002.137       6752.857
##   1565.1315_7.095 1577.6354_7.106 731.6047_7.112 1545.1554_7.122 1185.8481_7.12
## 2         7596.23         2974.81       5290.383        7436.127       7499.909
##   1186.3509_7.119 1174.8563_7.123 1543.1503_7.123 1542.1466_7.121
## 2        5486.907        3801.644        33650.15        35511.25
##   1566.6417_7.129 1544.153_7.124 1590.143_7.137 1506.089_7.132 1614.1417_7.109
## 2        2229.713       18147.69       14288.61       1726.009        4281.475
##   871.6892_7.141 1592.1596_7.145 1594.1676_7.138 785.5418_7.137 1507.0917_7.135
## 2       8669.539        26156.43        11131.22       2624.599        1453.571
##   1187.8642_7.137 1579.649_7.139 1187.3609_7.137 885.7047_7.143 842.6373_7.146
## 2        3963.236       3174.793        3975.496       5349.646       8487.827
##   1568.1618_7.14 1591.1473_7.14 1580.1526_7.144 834.6001_7.175 784.5879_7.141
## 2       64757.88       15067.01        3506.306          24930        1692172
##   1198.856_7.141 915.7154_7.143 901.6999_7.143 787.5943_7.141 807.5705_7.147
## 2       8771.701       17416.82       30570.23       41582.98       99010.71
##   1569.166_7.14 822.5404_7.148 806.5675_7.192 1574.148_7.172 1575.1514_7.174
## 2       62605.8        10181.6       197554.7       3456.569        2419.836
##   859.6529_7.212 925.6996_7.166 445.3675_7.185 1530.1458_7.188 1531.1493_7.186
## 2       8776.959       8828.329       12413.96        6399.987         4770.68
##   874.5538_7.197 692.5581_7.191 790.5744_7.193 1598.1451_7.197 1554.142_7.183
## 2       12562.07       7282.055       32107.72        1604.776       2480.263
##   831.57_7.208 812.5561_7.198 907.6886_7.2 746.5692_7.204 830.5666_7.214
## 2     28144.86       7984.733     1989.696       95167.72       48927.34
##   768.551_7.206 748.5272_7.208 842.5663_7.213 820.5846_7.213 864.5249_7.21
## 2      16492.49       5183.915       6109.288       24081.39      6938.945
##   827.6266_7.218 1548.0963_7.222 762.5038_7.219 740.5222_7.221 743.6054_7.227
## 2       3752.666        3244.971       13752.56       42976.86       7719.281
##   1546.0812_7.232 1547.0845_7.233 856.5819_7.24 1524.0983_7.244 1525.1027_7.243
## 2        3483.698        3488.121      6638.001        5453.528        4776.934
##   807.5706_7.257 834.6002_7.257 724.5272_7.284 797.5882_7.268 842.638_7.268
## 2       118741.5       39144.84       1910.823       35788.45         10514
##   796.5845_7.27 1542.1462_7.263 1581.1632_7.283 806.5675_7.265 874.5539_7.248
## 2      72564.86        10236.89        12460.12       221178.7       12604.81
##   1566.1444_7.237 859.6529_7.266 822.5405_7.274 807.0682_7.274 1604.1571_7.283
## 2        6496.696       9165.156       13790.86       14134.13        4398.783
##   902.7033_7.285 1198.8563_7.281 1187.8649_7.285 1580.159_7.285 901.6999_7.285
## 2       22156.02        15122.66        8691.572       16503.86       43110.04
##   1590.144_7.285 1579.6508_7.286 1199.3591_7.285 1602.1511_7.303
## 2       25340.23        6353.373        13333.36        3956.888
##   1619.1782_7.285 1187.3625_7.286 1591.1476_7.288 784.5881_7.29 871.6892_7.289
## 2        6223.696        5950.332           27672       2985056       12021.59
##   788.5964_7.29 1569.2427_7.289 1569.1673_7.289 786.6508_7.289 785.452_7.291
## 2      13002.81        7227.282        169802.8       14278.91      3061.687
##   1568.1636_7.29 915.7155_7.29 885.7047_7.291 787.5945_7.291 916.7188_7.29
## 2       183332.1      26279.46       9076.538       69423.93      16018.36
##   784.9268_7.293 1591.6495_7.292 1199.8618_7.293 1602.6436_7.296 1500.102_7.296
## 2       3556.974        7716.316         8550.24        3842.637       8130.538
##   1592.1607_7.297 1501.1056_7.297 1522.0815_7.299 1618.1731_7.3 1603.1486_7.299
## 2        53719.42        6924.185        3485.388      7398.908        6123.091
##   716.5222_7.301 1593.1654_7.298 1210.8558_7.304 1210.3541_7.309
## 2       29371.99        48039.57        6494.122        5208.963
##   1614.1426_7.311 1615.1461_7.313 1616.1569_7.316 1211.3587_7.312
## 2        6480.641        6684.407        6791.084        6283.586
##   808.5857_7.318 795.5728_7.343 1617.1623_7.316 1557.1645_7.322 1556.1613_7.323
## 2       326688.6       13748.49        5146.992        9951.845        12318.78
##   1616.1572_7.316 809.5888_7.318 939.7152_7.328 1601.1673_7.328 1600.1629_7.331
## 2        6791.084       164733.8       4276.156        2296.292        2488.047
##   810.5975_7.409 925.6999_7.337 1577.1677_7.349 816.589_7.342 830.5666_7.346
## 2       71589.98       6891.444        4408.132      9590.603        40156.2
##   1576.1637_7.357 792.5899_7.352 909.7046_7.351 814.5717_7.361 766.5377_7.38
## 2        4759.798       52424.32       3424.445       10382.27      27209.41
##   1550.1483_7.401 1574.1454_7.378 719.6006_7.392 1572.1304_7.394
## 2         4758.88        2446.968       4816.713        1660.778
##   1524.1333_7.404 824.6209_7.408 1551.1527_7.401 1562.1493_7.418
## 2        1292.635       2627.002         5710.29        2471.592
##   1550.1497_7.403 718.5737_7.412 1533.1433_7.416 788.5563_7.415 766.5755_7.417
## 2         4758.88       8682.358        3260.227       57011.77         263405
##   1532.1401_7.417 1562.1488_7.418 1563.1535_7.418 897.7047_7.419 883.6892_7.419
## 2        2746.207        2471.592        2596.255       6101.311       12916.71
##   818.5668_7.42 1554.1324_7.421 796.5848_7.423 1577.1679_7.401 1576.1634_7.391
## 2      22529.65        1357.014         107921        2470.981        3383.889
##   1539.1546_7.459 927.7143_7.45 812.6068_7.457 832.5817_7.451 811.6039_7.456
## 2        2163.313      6838.992       22156.73        31705.1       73096.53
##   810.6006_7.457 764.5195_7.458 742.5377_7.462 1538.1514_7.472 881.5145_7.469
## 2       134224.1       7721.074        22778.2        2572.721       6062.229
##   859.5323_7.471 840.5868_7.472 876.559_7.475 818.6053_7.48 1497.1674_7.486
## 2       5798.906        8956.46      11421.36      37945.43        1581.141
##   807.6359_7.483 750.599_7.485 1583.178_7.489 1582.1737_7.485 1583.1777_7.489
## 2       3831.017      503.6229       2480.384        2310.015        2480.384
##   1508.1468_7.513 794.568_7.494 889.6996_7.495 1541.1852_7.515 772.5855_7.501
## 2        3585.358      38519.91       7052.889        3199.927       226832.2
##   1500.1349_7.508 1509.1474_7.507 753.5881_7.505 1544.1615_7.508
## 2        2126.286        2092.075        85613.7        4649.395
##   1545.1646_7.511 1503.1775_7.514 1504.1822_7.513 822.5994_7.509 731.6073_7.511
## 2        4911.405        6527.674        3927.533       13095.05       454151.3
##   1463.2071_7.511 1462.204_7.513 1515.1602_7.514 1514.1517_7.514
## 2        3124.485       3205.003        4324.024        4498.192
##   1564.1662_7.508 1473.1717_7.517 1474.1752_7.518 859.6891_7.519
## 2        2016.174        4781.798        3421.702       8154.296
##   1507.1493_7.519 1506.1473_7.519 764.5566_7.52 873.7045_7.52 1508.1474_7.516
## 2        6633.252        6062.327      31794.88      4982.985        3585.358
##   1487.1502_7.525 1485.1447_7.523 742.5753_7.523 1518.1461_7.531
## 2        2613.703        2148.859       191131.1         2861.42
##   1484.1411_7.523 1486.1496_7.524 1465.1678_7.525 1466.1708_7.525
## 2        2470.002        1497.945        6654.898        6344.956
##   1477.1393_7.527 1523.1825_7.536 1476.1365_7.528 1553.1686_7.544
## 2        6809.455        2414.234        6057.234        3506.845
##   1526.1519_7.532 1534.154_7.527 1527.1556_7.532 1541.1773_7.535
## 2        4657.574       2007.279        3840.568        3371.827
##   1518.1432_7.534 1468.1317_7.534 1535.1558_7.533 734.5702_7.535
## 2         2861.42        6072.619        2250.025         353520
##   1469.1342_7.535 865.6997_7.537 851.6839_7.537 756.5512_7.537 821.6735_7.538
## 2        5004.616       5414.057       10778.74       54331.42       4345.489
##   1479.137_7.538 1490.1052_7.54 1540.1625_7.542 804.5937_7.544 794.5862_7.544
## 2       2990.767       2127.088        3679.243        3675.05       15818.02
##   1510.1552_7.549 1511.1587_7.551 1482.0893_7.554 1552.1644_7.555
## 2        4226.115        3603.847        2527.381        3953.855
##   835.5327_7.555 1499.1861_7.555 852.559_7.556 792.5899_7.556 1502.1505_7.558
## 2       4495.509        5060.153      7433.632       79604.41        8881.844
##   1503.1576_7.556 1500.1884_7.555 857.5144_7.562 1495.1496_7.564 849.6468_7.572
## 2        8585.501        4000.768       3715.037        2497.357       1906.305
##   1561.1726_7.566 814.5717_7.564 748.5274_7.569 1496.0473_7.572 1537.1733_7.573
## 2        2592.568       13302.22       33318.01        503.6229         6685.33
##   1517.1166_7.575 1516.1139_7.574 1560.169_7.579 1536.1705_7.575 770.509_7.576
## 2        2530.585        2080.433       2282.432        6401.749       9558.02
##   768.5909_7.579 1578.1799_7.626 899.7204_7.586 835.6299_7.591 790.572_7.588
## 2         425446        4914.774       8112.951        3593.34      71291.92
##   885.7047_7.587 1494.1454_7.592 806.5656_7.605 1512.1627_7.554 786.5917_7.604
## 2        18110.2         2098.24       15177.77         2484.35       12606.69
##   1491.1814_7.626 795.6347_7.605 1579.1825_7.633 832.582_7.631 941.7308_7.633
## 2         2028.23       4027.316        5770.362      32799.28       3788.433
##   902.5744_7.633 812.607_7.646 811.6042_7.645 810.6013_7.646 1578.1793_7.636
## 2         4986.1      33542.04       107926.3       204152.2         5323.63
##   850.5541_7.648 1544.1549_7.686 1592.1591_7.656 1181.7509_7.659
## 2       13692.65        23416.95        3815.001        3838.781
##   1187.8641_7.656 818.6355_7.665 1545.1606_7.669 744.5901_7.661 816.587_7.665
## 2        3432.469       8476.735        9823.201       122544.6      23278.81
##   1526.1435_7.632 1528.1659_7.665 1529.1702_7.665 782.5674_7.669
## 2          3219.7        13098.23        9945.977       204366.2
##   1605.1979_7.674 1571.181_7.67 1570.1764_7.672 1570.1773_7.672 911.7203_7.674
## 2        3559.159      25507.28         26085.3         26085.3       8083.822
##   1604.1948_7.675 798.5402_7.677 1163.3585_7.687 1538.1799_7.62 1539.1868_7.674
## 2        4078.128       12759.44         12428.9       3300.314        3109.004
##   1505.1707_7.678 1576.1618_7.678 1504.1669_7.68 878.7033_7.717 925.736_7.685
## 2        20141.23        4844.107       22547.92       25510.83      4708.039
##   1558.1829_7.689 1162.8568_7.691 794.6062_7.691 1558.1819_7.689
## 2        7927.069        16056.36       297145.4        6884.391
##   1588.1988_7.697 1548.6599_7.695 1589.2021_7.7 1543.1477_7.702 1554.1833_7.701
## 2        4674.947        3720.152      5487.492        43559.92        71208.03
##   1571.1809_7.671 1555.1868_7.702 1542.1448_7.703 1546.1714_7.763
## 2        25507.28        70354.03         40527.9        6810.405
##   1543.1481_7.702 1151.8656_7.71 771.5762_7.712 1531.6527_7.711 1151.3639_7.711
## 2        43559.92       17830.46       17433.56        13362.98         11389.1
##   1544.1542_7.691 761.302_7.716 1532.1582_7.713 861.7048_7.713 877.7_7.713
## 2        23416.95      4584.821        12936.25       20699.18    49098.13
##   1542.645_7.698 760.9218_7.716 1510.1192_7.742 762.5251_7.716 761.5191_7.716
## 2       9268.252       6645.419        2446.165       5528.137       10925.86
##   847.6893_7.716 761.3781_7.717 762.6491_7.715 760.9763_7.718 761.4062_7.717
## 2       24082.52       5340.275       22571.04       4674.224       6146.495
##   761.329_7.717 761.0763_7.717 761.4545_7.719 760.5883_7.719 762.5433_7.717
## 2      5142.379        3327.18       6169.562        4489468       5709.717
##   761.4437_7.717 761.1927_7.717 763.5945_7.717 1521.1684_7.718 1520.1651_7.717
## 2       6169.562       4225.203       111291.4        488071.2        529120.6
##   891.7156_7.718 761.3681_7.718 1531.1683_7.726 1535.1253_7.718 1534.1209_7.719
## 2       34318.62       5340.275        6923.904        5424.926          6476.6
##   761.2595_7.716 761.4141_7.718 760.5459_7.719 761.3174_7.717 1583.089_7.72
## 2       4221.082       5045.675       11911.66       4070.718      7703.644
##   1584.0928_7.721 762.5032_7.719 1521.243_7.719 1629.2009_7.725 1628.1971_7.724
## 2        7824.315       5498.319       19060.62        6422.565        6269.854
##   774.5423_7.725 1608.1354_7.743 1568.1599_7.728 1542.1456_7.705
## 2       11221.32        1272.199        5344.125         40527.9
##   1568.6597_7.736 1569.1611_7.737 1569.1618_7.737 1595.1823_7.739
## 2         6495.05        7725.889        7725.889        98361.68
##   1594.1789_7.739 1588.1975_7.7 1188.8722_7.741 1597.1881_7.738 1579.6524_7.748
## 2        107510.6      4674.947        6300.311        22405.04        4470.258
##   1580.1749_7.748 835.655_7.749 834.602_7.751 1669.196_7.752 1668.1927_7.752
## 2         5792.39      11716.94      448794.2       7539.902         7337.15
##   1616.16_7.754 1617.1626_7.754 1199.864_7.757 1518.2006_7.758 1517.1985_7.759
## 2      7962.745        8248.731       6011.038        14714.34        14677.28
##   965.7308_7.761 1540.1703_7.761 1591.2114_7.764 828.6106_7.766 1592.2118_7.764
## 2       4966.207        5146.007        2566.139       6108.895        3137.566
##   818.6356_7.752 819.5737_7.769 951.7153_7.77 1531.181_7.771 1530.1768_7.745
## 2       10548.59       5129.065      8069.892       7283.269        7336.854
##   757.6221_7.776 856.5823_7.778 798.5402_7.73 1484.1047_7.781 1559.1256_7.781
## 2       173236.7       37189.68      12759.44         12958.7        3212.683
##   1558.1201_7.783 1485.108_7.781 1546.1749_7.78 1604.1937_7.739 780.5921_7.79
## 2        3719.182       12755.68       6810.405         2881.03      21050.65
##   1506.0868_7.79 1547.1793_7.789 1507.0899_7.79 1580.1032_7.792 837.614_7.764
## 2        7164.45        6038.432        6378.97        1895.618      20684.03
##   1481.1402_7.792 858.5914_7.792 825.6473_7.81 821.6234_7.811 820.6201_7.811
## 2        1800.823       7071.552      3993.225       12139.44       22466.33
##   902.5743_7.805 724.5277_7.806 1448.0465_7.808 1470.0283_7.811 770.6049_7.812
## 2       5288.838       82317.88        2385.798        1235.015       53227.27
##   825.6476_7.811 802.5717_7.81 746.5091_7.814 811.6292_7.819 770.6048_7.813
## 2       3993.225      3054.885       22823.37       9954.615       53227.27
##   798.6003_7.83 860.6149_7.833 792.5888_7.837 1532.1012_7.882 820.5823_7.852
## 2       34042.7       6221.181       18303.95        2098.229       8507.365
##   783.6368_7.847 805.6187_7.848 745.6202_7.852 818.6049_7.793 806.611_7.876
## 2       31443.32       9091.662        13998.8       8690.408      4951.175
##   876.5695_7.88 878.5742_7.89 756.5904_7.889 748.5844_7.89 772.5245_7.894
## 2      7075.782      5127.966       9227.396      18335.36       9190.083
##   1547.1803_7.856 700.5274_7.902 1486.1173_7.882 826.6326_7.901 820.6202_7.907
## 2        5399.436        21380.6        3084.073       1115.182       24864.75
##   1578.178_7.924 854.6305_7.919 900.5692_7.918 1511.123_7.922 1510.1192_7.922
## 2       2589.248       4165.847       8471.465       4173.432        4908.784
##   1558.1745_7.924 733.6212_7.929 1566.1816_7.932 734.6246_7.927 733.6215_7.929
## 2        3018.691       26322.75        1988.402       13073.23       26322.75
##   1567.1909_7.931 1570.1778_7.936 848.556_7.937 885.6686_7.937 1544.2137_7.94
## 2        2143.826        10944.23      11359.54       7438.577       3019.196
##   832.5829_7.939 1543.2104_7.94 822.5985_7.942 750.5434_7.943 1582.1177_7.942
## 2       190289.3        2826.24       7101.019       42114.63        6270.005
##   1238.3816_7.948 1604.1947_7.949 1592.1862_7.949 1237.8801_7.949
## 2        11928.05        5575.021        3663.058        12679.16
##   811.0052_7.959 1642.1751_7.953 1643.1784_7.953 1605.1986_7.95 1561.1392_7.955
## 2       4200.295        22001.81        26935.08       5968.865        15708.44
##   812.6663_7.96 822.0921_7.956 1227.3893_7.956 1560.1357_7.954 1632.1881_7.956
## 2      20483.73       13487.08        8973.357        17124.43        12940.04
##   1591.1826_7.956 927.7157_7.955 812.5189_7.959 1226.887_7.956 821.5898_7.956
## 2        3305.585       47009.25       3487.065       13389.33       12952.92
##   813.6106_7.959 1225.88_7.956 1226.3841_7.957 941.7313_7.957 1631.6823_7.958
## 2       105242.4      9936.575        11729.91       34206.33        12720.36
##   1622.2784_7.959 897.705_7.958 911.7206_7.958 811.2977_7.959 810.9483_7.96
## 2        8327.534       16258.5       12577.91       2953.238      8446.533
##   810.6041_7.959 1590.1749_7.958 1621.1991_7.959 811.4149_7.961 814.6127_7.96
## 2        3797555        2573.641        291290.3       3379.271      19150.58
##   1620.1954_7.96 811.5374_7.96 811.4537_7.958 1621.2757_7.96 1631.1866_7.959
## 2       288335.2      7926.898       3951.149       14227.17        10745.73
##   811.3445_7.959 812.545_7.958 1604.1946_7.949 1225.3783_7.964 1606.2002_7.968
## 2       2689.355      4522.855        5575.021        8367.904        4865.028
##   1620.6864_7.972 820.5825_7.912 1215.3898_7.986 1580.196_7.986 1581.2003_7.986
## 2        7794.148       7906.429         8153.81       8360.784        8650.421
##   1214.8875_7.988 1536.1354_7.989 1619.1791_7.991 1537.1392_7.989
## 2        9454.278        7501.669        25151.99        7450.519
##   1214.3846_7.993 771.6088_7.993 1618.1753_7.994 770.6056_7.993 1646.2057_7.997
## 2        9955.204       21877.81        19924.52       45203.06        9875.249
##   1608.188_7.997 1597.1987_7.999 1607.6828_7.999 1596.195_7.999 1203.3894_8.009
## 2       9616.779        238179.7        8933.791       237418.1        8534.023
##   1647.2105_8.001 1597.2743_8.003 1213.88_8.002 1607.1867_8.003 811.6598_8.009
## 2        9716.373        10932.16      8690.057        9580.733       15617.21
##   1606.677_8.009 1202.8865_8.011 1556.1974_8.013 1557.2015_8.015
## 2       7183.564        10947.61        7585.335        8709.026
##   1625.2149_8.037 810.6564_8.03 1595.6827_8.019 1547.1806_8.02 1191.3906_8.062
## 2        12292.92      27826.25        14310.02       6703.286        15374.79
##   1546.1772_8.02 918.7347_8.057 788.6651_8.049 1576.2067_8.049 788.4624_8.049
## 2       7400.991       26834.83       28974.59        47163.59       5721.841
##   1202.3827_8.089 787.4429_8.052 787.1747_8.049 901.7361_8.046 788.4791_8.049
## 2        15047.35       8742.474       5992.747       3997.741       6112.382
##   787.1568_8.05 1573.2005_8.05 787.0639_8.049 787.6075_8.051 787.4152_8.05
## 2      7667.641       693332.7       5572.202        2972382      7381.672
##   787.4054_8.05 789.0553_8.052 788.4066_8.049 787.4705_8.05 887.7206_8.05
## 2      10284.95       7108.641       5121.138      9619.832       17676.8
##   789.6103_8.05 787.2199_8.049 788.5214_8.05 788.6624_8.05 787.246_8.049
## 2      137969.9       7135.861        7705.8      28974.59      5294.002
##   788.3967_8.051 786.6043_8.051 787.2651_8.051 787.4549_8.049 788.3339_8.051
## 2       4785.516        5057976       7128.331       7922.134       4397.506
##   787.2508_8.05 1623.2097_7.994 787.2891_8.052 788.4473_8.05 789.0076_8.052
## 2      7128.331        67560.01         9301.7       5244.83       7598.059
##   1574.2787_8.051 787.3251_8.05 790.6125_8.05 787.3567_8.05 787.1311_8.049
## 2        19970.31      8355.461      24993.36      8801.472       4919.229
##   788.3678_8.051 787.0988_8.052 787.394_8.051 788.5317_8.052 788.5585_8.05
## 2       4608.768       4709.151      6810.915         7705.8      8609.776
##   1572.1972_8.051 788.4344_8.049 1624.213_8.033 787.2834_8.051 787.1808_8.051
## 2        713128.7       5311.012       31627.44        6348.56       5992.747
##   787.2998_8.051 787.0264_8.052 787.5336_8.052 787.3787_8.051 873.705_8.052
## 2         9301.7       5334.682       16162.11       8637.388      21801.81
##   787.4344_8.052 786.998_8.053 789.039_8.054 786.5592_8.053 836.6167_8.053
## 2       8742.474      5534.201      8507.692       13340.05       164887.3
##   837.6197_8.054 788.2955_8.051 917.7314_8.057 1574.2746_8.051 1625.2172_8.052
## 2       92194.16       4771.157       41920.64        19970.31        12292.92
##   788.4171_8.052 787.5853_8.055 1577.208_8.052 1583.684_8.06 1190.8891_8.06
## 2       5784.904       11014.68       12456.68      18803.38       19758.05
##   1584.1871_8.061 1504.14_8.066 809.6525_8.065 1542.1805_8.073 798.0928_8.066
## 2        21277.47      6772.122       35540.13        1776.127       17759.56
##   1580.1886_8.021 903.7157_8.069 797.5914_8.07 1512.1341_8.076 1513.1387_8.078
## 2         7128.25       54982.04      20372.87        4902.032        4631.781
##   1594.1759_8.08 1596.1936_8.011 1595.1798_8.081 1015.8403_8.077
## 2       37922.51        237418.1        43863.75        4834.177
##   1534.1768_8.083 1578.1482_8.087 1579.1512_8.088 1531.2124_8.09
## 2        2277.115        8681.579        7062.498       2929.316
##   1538.1459_8.043 1560.1917_8.095 1561.1945_8.097 1201.8801_8.097
## 2        4576.042        6615.864         6840.08        19030.65
##   748.5844_8.099 1601.1358_8.098 792.5534_8.102 838.6248_8.065 858.5976_8.109
## 2       7808.411         2936.89       20987.08       27926.53           9422
##   1505.1673_8.109 844.6529_8.109 1568.1908_8.106 1504.1651_8.11 824.5559_8.11
## 2        4884.837       9276.462        2051.227       6692.307      11680.59
##   861.6684_8.111 808.5829_8.112 745.6214_8.115 814.5355_8.122 876.5697_8.13
## 2       8620.017       225450.7       33230.93       4392.017      14577.01
##   797.6166_8.131 812.615_8.136 782.6048_8.136 797.622_8.139 813.6188_8.138
## 2       13172.68      55408.08       13588.56      13172.68       30250.12
##   718.5742_8.138 796.6199_8.138 740.5559_8.156 804.5876_8.154 803.6626_8.177
## 2       35830.49       27612.89       7921.292       3303.463       3644.082
##   738.543_8.181 831.6353_8.196 794.6054_8.214 816.5873_8.216 772.5244_8.234
## 2      3907.031       7501.353       17375.99       5054.953       6606.474
##   748.5258_8.233 750.5444_8.236 726.5431_8.239 726.5428_8.237 796.5824_8.278
## 2       5351.612       14193.02       10719.58       10719.58       15188.23
##   742.5718_8.28 774.6005_8.288 720.5897_8.289 840.5869_8.291 818.6044_8.299
## 2      10703.92       62710.81       45988.07       2605.901       11294.86
##   812.518_8.311 1559.0847_8.317 790.5354_8.318 1536.1002_8.319 869.6735_8.319
## 2      11549.13        3963.939       45920.63        5456.714       8030.849
##   855.6577_8.319 1558.0818_8.319 768.554_8.322 766.5713_8.323 1537.103_8.32
## 2       10435.99        4421.086      137888.4       9783.301      6037.892
##   861.7043_8.325 1512.129_8.321 744.5895_8.327 859.53_8.35 1603.1463_8.354
## 2       3211.882       1559.188       44567.25     5301.65        4844.428
##   1602.1428_8.354 1581.1647_8.356 771.6366_8.357 577.5185_8.358 1580.16_8.36
## 2        4260.903        6483.572       19273.06       9046.008     6051.538
##   837.5477_8.363 854.574_8.362 844.6193_8.384 1556.1852_8.372 796.621_8.383
## 2       6388.882       5900.62       2343.371        2861.209      42591.81
##   1598.2072_8.389 1599.2108_8.39 797.6421_8.39 1609.235_8.392 870.6724_8.39
## 2         2189.45       2292.629      22040.94       2434.688      8020.474
##   798.6518_8.391 768.5859_8.446 1608.2268_8.392 834.5987_8.394 913.7362_8.395
## 2       9237.925       16053.65        2900.167       153612.4       7233.695
##   850.5715_8.394 1240.9027_8.395 1647.2081_8.395 1636.2128_8.395
## 2       7632.641        5042.266        5742.002        1433.161
##   1646.2051_8.395 1241.4043_8.395 870.6699_8.393 823.6064_8.398 943.7468_8.396
## 2        4859.879        3468.615       8020.474       7488.811       16223.18
##   1625.2283_8.396 899.7208_8.396 812.6186_8.396 902.5848_8.397 1624.2248_8.397
## 2        28604.91       6616.074        1047428       11303.64         23280.4
##   829.6782_8.401 1229.9108_8.397 929.7313_8.398 887.6842_8.398 928.5901_8.4
## 2       2905.943        1786.848       30229.99       9576.363     3768.261
##   1488.1014_8.407 1579.148_8.401 1556.1645_8.4 812.5816_8.403 1578.1446_8.401
## 2        1599.076       6521.392      8926.728       4463.521        5768.261
##   744.5536_8.408 1558.2047_8.408 831.6573_8.408 836.6141_8.438 766.5356_8.411
## 2       67014.39        5426.738        6645.16          67513       17174.81
##   1559.2145_8.411 1648.2179_8.411 835.6663_8.407 1649.2233_8.415 797.6453_8.401
## 2        4953.904        4564.862       3489.756        4294.236       18578.69
##   800.616_8.426 812.5434_8.427 746.6055_8.435 836.6148_8.439 862.6306_8.44
## 2      7890.593          26353       87903.04          67513      4183.623
##   834.5249_8.443 863.7199_8.448 953.7312_8.446 768.587_8.451 858.5977_8.453
## 2       8687.038       4957.399        2759.53      16053.65       14241.17
##   838.626_8.451 820.6211_8.471 842.6021_8.473 827.663_8.483 872.6526_8.477
## 2      12044.27       19120.01       5486.396      2012.232       2843.092
##   732.589_8.53 822.6349_8.528 816.5872_8.538 911.7203_8.543 1681.1596_8.548
## 2      2401.35       13368.51       13415.11       2542.075        1467.894
##   794.6057_8.544 846.6354_8.545 824.616_8.544 627.5345_8.545 904.5911_8.546
## 2        55114.2       4117.595      8617.461          51406       123015.3
##   887.564_8.547 974.6686_8.55 909.5461_8.55 988.6841_8.551 931.5277_8.561
## 2      59375.29       16389.7      76121.21       12244.31       18477.89
##   628.54_8.549 682.5516_8.574 682.5424_8.581 797.6383_8.603 774.5401_8.626
## 2     26479.12       3619.219       2558.001       5545.024       4538.035
##   800.6163_8.637 772.6196_8.643 762.6465_8.683 770.6056_8.664 887.7201_8.662
## 2       17393.14       18083.74       11254.51       34141.53       3043.479
##   879.715_8.665 762.6007_8.665 762.6003_8.665 792.5873_8.665 784.5827_8.668
## 2      3347.962       69766.69       69766.69       7310.066       14434.09
##   783.6251_8.708 760.6411_8.71 761.6436_8.707 603.5342_8.674 885.5458_8.676
## 2       12006.47      178180.8        47402.3       12115.92       12543.16
##   880.5906_8.676 881.5938_8.68 863.5639_8.681 863.6636_8.706 798.5401_8.707
## 2       17983.94      8863.724       10738.46       3641.466       12199.61
##   776.5588_8.709 864.7169_8.689 782.6223_8.717 759.6382_8.714 781.6192_8.718
## 2       31320.01       1566.179       37748.96       377947.8       79606.58
##   913.7359_8.729 818.603_8.731 796.6218_8.731 1592.2329_8.736 1556.2518_8.736
## 2       10978.33      33741.27       184381.4        1825.225        2584.336
##   927.7519_8.733 1555.2487_8.735 1581.2641_8.744 1518.2654_8.731 1544.281_8.743
## 2       4942.718        2985.768        1573.402        2188.292       1780.539
##   812.6149_8.745 787.6592_8.744 785.6536_8.747 786.6565_8.746 807.6347_8.75
## 2       29369.25       24286.89       155052.9        84624.2      34807.37
##   808.6375_8.751 792.551_8.764 770.569_8.759 1096.0441_8.765 1585.2312_8.763
## 2        19958.4      3824.715      10167.52        4770.117         2009.13
##   1547.245_8.769 813.6183_8.757 1584.2261_8.767 872.652_8.777 838.6321_8.779
## 2       1813.769       15552.54        1495.615      3547.788        71823.5
##   955.7465_8.781 860.6133_8.785 802.5738_8.798 812.6135_8.749 844.6182_8.806
## 2       4056.542       19085.75       2034.857       29369.25       9798.069
##   822.6365_8.807 1626.2381_8.815 772.6209_8.839 772.6206_8.839 1560.2277_8.857
## 2       37384.18        1795.634       36643.13       36643.13        2528.111
##   895.575_8.852 1610.2416_8.847 1561.2317_8.857 868.5564_8.853 1560.2279_8.857
## 2      9122.681        1606.627        2096.769       8738.109        2528.111
##   970.718_8.858 878.5852_8.857 1205.4054_8.863 1193.9116_8.867 1204.9038_8.864
## 2      2072.045       15522.63        3425.485        2413.895        6387.432
##   889.7358_8.865 799.6069_8.865 810.5988_8.865 1204.4035_8.865 846.6352_8.854
## 2       6697.935       6983.009       162977.6        4851.083       3632.331
##   826.5716_8.868 1599.2099_8.871 788.6177_8.867 875.7207_8.87 919.7465_8.87
## 2       8242.287         6221.98       865810.6      7441.585       15927.8
##   1600.2146_8.869 905.7311_8.871 1598.2061_8.871 1576.2251_8.873
## 2         4016.72       29489.88        5958.527        23088.73
##   1577.2283_8.872 863.6838_8.874 846.6653_8.88 846.6641_8.878 920.75_8.87
## 2        23334.67       7788.028      6441.184       6441.184    8393.018
##   862.6249_8.847 798.635_8.894 1562.15_8.919 1541.1702_8.921 1540.1672_8.92
## 2       2585.278      6228.352      2620.241        3824.149       4577.397
##   1574.2638_8.923 1573.2611_8.925 1599.2772_8.928 1600.2791_8.929
## 2        6542.399        6040.388        5463.161        5027.115
##   752.5592_8.969 1540.1695_8.922 634.54_8.941 1424.1384_8.941 835.6566_8.968
## 2       121846.2        4577.397     11649.02        1106.273       16823.72
##   807.6349_8.948 1570.2972_8.954 1571.3005_8.952 785.6542_8.953 1618.2939_8.956
## 2       100125.8        4251.078        3762.899       522479.3         2913.59
##   1619.2973_8.957 1597.3162_8.956 1226.4736_8.96 1596.3127_8.957 902.7673_8.957
## 2        2636.041        6456.792        2384.73        7183.553       4785.488
##   864.6463_8.959 1622.3273_8.96 1623.3314_8.96 1538.2067_8.962 811.6703_8.961
## 2       3853.001       3328.855       2920.397        8198.008       452180.2
##   1537.2035_8.961 942.7986_8.961 928.7831_8.964 1563.2185_8.966 1564.2222_8.965
## 2        8158.631       3112.762       4864.024        6779.179        7188.461
##   1559.1849_8.966 820.6364_8.966 1560.1891_8.966 1505.1127_8.971 752.5594_8.973
## 2        4831.348       5543.612        4781.248        3282.363       121846.2
##   833.6507_8.971 1504.1092_8.971 1585.2008_8.971 1586.2043_8.972 839.6622_8.973
## 2       90670.38        3353.871        3973.039        4660.113        11185.8
##   1526.0908_8.976 1527.094_8.977 853.6786_8.975 853.6786_8.974 774.5404_8.982
## 2        2261.368       2447.814        6491.64       6065.305          33071
##   901.6373_8.994 796.5222_9.007 773.6516_9.025 848.6521_9.028 870.6336_9.039
## 2       5242.931       6539.751        5768.31       10455.57       3922.008
##   778.5756_9.056 800.5558_9.058 639.4955_9.064 634.5399_9.061 814.6319_9.087
## 2        21589.4       9973.236       9008.702       17308.44       45744.32
##   728.5588_9.081 772.5221_9.085 815.6606_9.084 814.6318_9.087 836.6136_9.086
## 2       36172.36       4829.609       6896.445       43821.04       12368.03
##   815.6352_9.088 976.684_9.115 889.5795_9.114 889.5796_9.114 906.6063_9.114
## 2       23855.86      3964.211       10294.85       10294.85       19006.69
##   911.5614_9.119 907.6093_9.114 761.6526_9.142 860.6131_9.152 838.6301_9.147
## 2       15033.86       9790.684       13240.48       3334.685       10393.55
##   761.6524_9.144 783.6349_9.156 824.6527_9.167 798.6374_9.202 787.6674_9.202
## 2        14064.4       4326.388       3069.503       10325.25       11397.39
##   837.6831_9.217 746.5685_9.239 836.6135_9.214 1134.7862_9.281 805.6784_9.291
## 2        2259.97       23541.21       5999.759        2264.969       3160.882
##   831.694_9.318 799.6686_9.32 821.6501_9.321 853.6758_9.321 825.6815_9.335
## 2      9682.384      64151.51       19314.07       4403.143        4570.61
##   795.6345_9.346 773.6531_9.348 796.6374_9.346 829.6788_9.393 790.5588_9.389
## 2       31471.45       106267.2       15768.11       2430.643       29043.04
##   812.5409_9.393 835.6652_9.434 813.6834_9.437 911.5609_9.505 748.6211_9.504
## 2       14324.15       2862.271        5873.96        3171.01       8226.154
##   906.6086_9.509 830.6471_9.551 825.6817_9.564 825.682_9.561 825.6778_9.575
## 2       4015.066       2937.108       5497.752      5497.752       5497.752
##   799.6692_9.585 821.6503_9.585 889.6375_9.586 824.6537_9.596 865.5795_9.626
## 2       126674.4       40410.24       5655.457       8026.033        9856.34
##   882.6062_9.627 605.5497_9.629 887.5614_9.632 610.5401_9.655 874.6674_9.727
## 2       11044.41       12343.13       13329.28       28221.02       3739.477
##   946.718_9.767 822.6412_9.776 994.7158_9.773 972.7339_9.773 610.5401_9.784
## 2      4369.626       3652.028       3394.066       6745.506       26610.01
##   575.5027_9.782 775.6661_9.814 848.6521_9.817 802.5715_9.823 780.578_9.823
## 2       7375.394       3828.128       6023.061        7545.51      20116.21
##   837.6724_9.906 636.5558_9.845 877.6374_9.86 1454.1868_9.88 1428.1716_9.868
## 2       21972.99       42947.85      7760.845       2822.004        3884.678
##   790.6308_9.873 836.6696_9.91 809.6507_9.876 825.6241_9.886 1229.9993_9.9
## 2       10953.72      68698.66       142217.1       5103.176      5663.727
##   1216.4896_9.892 1596.3093_9.895 822.6567_9.894 904.7835_9.894 787.6701_9.896
## 2        6419.074        2667.233       9440.671       7669.869       764947.4
##   918.7987_9.897 1575.3325_9.895 1597.3131_9.894 1216.9911_9.896
## 2       3990.714        9844.885        4571.203        6257.887
##   1574.3292_9.897 1610.32_9.892 1218.0011_9.9 1205.4975_9.898 1601.3486_9.899
## 2        9586.805      2037.454      2942.346          2605.5        15603.96
##   1624.8418_9.901 1612.335_9.9 1600.3452_9.899 1622.3263_9.899 1204.9948_9.897
## 2        1690.118     3212.694        17856.85        6902.972        2218.285
##   1623.3297_9.9 1626.36_9.901 1229.4977_9.901 823.1597_9.895 1636.3352_9.901
## 2       6103.94      7419.634        5963.986       9982.173        1853.384
##   1218.5051_9.902 813.6866_9.902 1611.8348_9.901 851.64_9.902 1648.3413_9.902
## 2        2684.094       690934.4        1809.095      6419.02        2774.843
##   944.8145_9.903 1635.8345_9.901 930.7989_9.903 1627.364_9.902 1231.5117_9.905
## 2       4155.017        2170.793       6697.064       7962.128         1438.79
##   1649.3448_9.904 1242.5053_9.906 835.6666_9.91 1637.3363_9.913 1624.3343_9.899
## 2        2797.678        2477.214      129321.4        1596.268         4526.23
##   903.6531_9.918 1454.1867_9.904 1455.1905_9.914 811.657_9.886 900.6836_9.96
## 2        7390.53        2822.004        2837.323      22864.78      3004.962
##   824.6526_9.963 816.6467_9.969 846.6342_9.978 850.668_9.982 1260.0333_9.997
## 2       22027.61       9653.991       6442.004      21643.29        1689.841
##   636.5558_9.995 877.6374_9.988 872.6492_9.995 641.5114_9.997 874.6655_10.042
## 2       56788.64       7760.845       6610.988       25429.38        4552.474
##   1285.8106_10.085 1237.8134_10.084 1259.7951_10.087 1263.8289_10.086
## 2          3408.22         6988.395         5009.238          4262.39
##   848.6549_10.11 839.6989_10.178 802.5716_10.204 893.6243_10.204
## 2       3186.075        6096.698        5188.358        5797.206
##   903.6531_10.207 780.5795_10.219 835.6664_10.22 1242.5051_10.22
## 2        8449.854        22486.07       103869.8         2082.82
##   813.6854_10.226 1649.3444_10.225 1626.3599_10.225 1627.3632_10.227
## 2        381365.5         2159.219         3409.696         2932.786
##   930.7988_10.228 993.7984_10.238 876.6835_10.245 850.6799_10.276
## 2        3466.205        1542.351        22272.34        10435.37
##   827.6998_10.277 849.6809_10.279 790.6874_10.339 837.6789_10.324
## 2        60154.34        14998.08        22034.11        9877.133
##   789.6837_10.343 817.7051_10.344 1263.8289_10.349 789.6834_10.343
## 2        40494.28        6994.667         5731.129        40494.28
##   1136.8021_10.373 784.6654_10.38 810.6812_10.387 827.6999_10.389
## 2         3619.193       49122.83        25851.02        26534.71
##   1392.1715_10.401 823.6664_10.409 918.7994_10.407 1624.3414_10.412
## 2           3190.6        62043.82        4696.219         2813.871
##   801.686_10.409 1602.3605_10.41 1603.364_10.411 612.5558_10.456 577.5187_10.46
## 2       692194.6        11764.94        12013.23        77427.25       32375.08
##   1251.8293_10.466 576.5712_10.467 1273.811_10.468 638.5716_10.5
## 2         8640.006        5095.905        3408.723      165529.4
##   827.6999_10.502 828.702_10.497 603.5343_10.505 1061.8543_10.506 974.75_10.504
## 2        25615.83       15173.97        34431.88         3343.002       11808.7
##   1281.8397_10.519 620.5974_10.522 602.5849_10.522 646.6126_10.526
## 2         6460.258        18829.71        5623.001        11654.99
##   798.681_10.534 876.6836_10.537 837.6818_10.567 1631.3955_10.568
## 2        32928.6        16258.83        39000.64         28180.23
##   1630.3921_10.568 815.7025_10.568 932.8152_10.571 1693.3899_10.581
## 2         28998.01         1358551        14281.75          3901.89
##   1694.3938_10.581 1265.8449_10.582 1266.8482_10.583 878.6996_10.587
## 2         3288.189         12453.42         8237.125        66347.16
##   852.6843_10.599 812.6971_10.626 634.6121_10.629 794.6868_10.627
## 2        28785.61        79744.41         13066.2        8050.009
##   829.7155_10.657 604.6026_10.678 622.6132_10.675 648.6287_10.678
## 2        93435.59        24810.28        92957.06         65970.8
##   630.6177_10.682 750.7353_10.695 766.6703_10.657 636.6288_10.754
## 2        16101.65        13713.27        2498.589        61143.04
##   618.6182_10.756 1298.1983_10.827 1297.1947_10.828 1301.284_10.831
## 2        18534.63         14894.16         15831.92        9458.521
##   650.6449_10.83 632.6339_10.83 1300.2808_10.831 1323.2657_10.832
## 2         231980       61316.71          10455.6         7166.766
##   1322.2624_10.832 737.7496_10.83 751.765_10.833 652.6596_10.839
## 2         7450.369       99716.04       43819.77        29194.06
##   698.6194_10.879 369.3508_10.879 822.6568_10.855 706.612_10.9 367.3357_10.902
## 2         7585.75        7600.824        1118.715     503.6229        2734.666
##   1312.1372_10.911 536.437_10.971 537.4409_10.971 794.7051_10.958
## 2         1260.354       15194.12        6173.038        1532.064
##   764.6932_10.985 750.6771_10.977 723.7681_10.986 990.7559_11.04
## 2         3286.64        4052.782        6541.254       503.6229
##   916.7378_11.078 986.8154_11.075 966.7541_11.082 790.6914_11.103
## 2        4055.856        2141.574        1308.728         14667.1
##   871.6776_11.111 866.723_11.11 897.6938_11.125 1012.8318_11.122
## 2         1971.72      11646.06        2771.518         2667.541
##   942.7538_11.123 816.7069_11.125 886.7839_11.127 992.7701_11.13
## 2        7002.996        24550.12        8966.745       1220.198
##   892.7383_11.126 947.7089_11.125 1026.8485_11.128 893.7418_11.129
## 2         13834.8        1734.781         1478.241        9333.282
##   847.6774_11.153 842.723_11.155 912.7986_11.157 1077.8922_11.168
## 2        4512.683       35983.47        12624.66         3036.969
##   1052.864_11.17 923.7092_11.174 988.8322_11.176 918.754_11.178 873.694_11.187
## 2       1462.379        4076.415        5921.241       24098.13       7523.448
##   873.6937_11.187 1038.8471_11.18 868.7389_11.19 938.8159_11.193 537.4408_11.2
## 2        7523.448        1416.362       59879.36        20733.04      10269.49
##   536.437_11.201 973.7251_11.18 899.7091_11.203 964.8324_11.205 978.8481_11.206
## 2       22663.33       1255.917        9732.242         18701.9        8140.438
##   894.7548_11.21 895.7581_11.209 949.7248_11.221 792.7074_11.221
## 2       68870.35        46552.75        3564.008           53668
##   1028.8632_11.223 1014.8477_11.222 944.7698_11.221 994.7855_11.222
## 2         2733.207          5224.73        21662.38        1416.049
##   965.8354_11.205 1064.8634_11.211 823.6785_11.238 818.7234_11.241
## 2        14816.66         1068.808        12250.72        122100.3
##   888.8006_11.24 979.8509_11.21 880.718_11.248 1651.3848_11.251 966.8463_11.305
## 2       42924.87       7065.987        25625.6          1242.25        78760.45
##   1676.3968_11.262 1677.4008_11.264 849.6939_11.262 958.844_11.264
## 2         1796.038          1726.64        20410.24       4713.466
##   972.8574_11.264 844.7397_11.265 914.817_11.265 928.8321_11.265 984.86_11.28
## 2         2451.91        204800.8       66042.93        29528.29     4351.066
##   970.786_11.284 875.7095_11.292 870.7558_11.296 940.8331_11.297
## 2       7937.148        30375.43        324633.8        108197.9
##   925.7249_11.296 954.8481_11.299 996.8_11.288 990.8484_11.304 920.7705_11.302
## 2        10130.74        44881.93     1424.071        21417.84        85782.57
##   967.8518_11.315 1004.8642_11.302 896.7714_11.316 980.8638_11.316
## 2        52803.07         8446.431        248298.5         32281.8
##   980.8635_11.316 966.8486_11.316 896.7712_11.316 901.7251_11.318
## 2         32281.8        78760.45        248298.5        24962.91
##   832.7384_11.319 1030.8788_11.336 1016.8631_11.335 946.7855_11.335
## 2        20185.07         5249.511         12976.75        45298.57
##   951.7402_11.339 863.7092_11.343 858.7545_11.345 972.8_11.345 1678.4082_11.291
## 2        5979.087        4331.861        38934.54     6095.989         1496.688
##   897.7743_11.318 794.7232_11.353 864.8008_11.354 1603.3857_11.359
## 2        159883.8        83499.66        35786.23          1324.06
##   1602.3814_11.358 884.7697_11.358 1629.4012_11.367 1628.3971_11.365
## 2         1567.764         22661.8         2671.462         3046.245
##   820.7395_11.368 942.849_11.418 927.7399_11.374 1654.4129_11.376
## 2        301490.2       478812.6        12192.02         4399.808
##   1655.4167_11.377 934.7878_11.376 992.8634_11.384 851.7097_11.384
## 2         4814.808        3561.133        46830.57        37883.41
##   968.863_11.436 1006.8788_11.385 1680.4284_11.386 922.7862_11.386
## 2       199250.5         19874.99         8051.829        174604.8
##   1681.4323_11.387 846.7559_11.39 930.848_11.39 916.8333_11.39 872.7731_11.418
## 2          7821.17       682281.1      114990.1       257165.2         1616522
##   877.7253_11.418 956.8644_11.419 942.8496_11.419 872.7324_11.418 924.8_11.503
## 2        60751.11        203498.6        478812.6        9127.002     217531.5
##   898.7872_11.44 982.8793_11.44 968.8643_11.442 971.877_11.497 903.7406_11.457
## 2       669157.7       92480.07        199250.5       17941.21        37241.99
##   834.7543_11.459 1018.8786_11.465 948.8005_11.465 1032.894_11.465
## 2        47546.99          10522.3        41912.89        4827.447
##   953.7558_11.474 974.8146_11.474 860.7704_11.477 865.7253_11.477
## 2        3773.431        6077.163        140002.2        11221.68
##   936.8015_11.483 891.7406_11.489 886.7858_11.492 929.7558_11.499
## 2        4311.123        8177.318        89998.27        14997.75
##   994.8787_11.507 1020.8931_11.508 1008.8943_11.508 1034.9093_11.511
## 2        61630.42         7597.849         25257.67          3466.29
##   1633.4321_11.51 924.8016_11.511 950.8156_11.512 1628.4765_11.515
## 2        2025.455          228336        27785.38         1713.184
##   1658.4443_11.517 892.8326_11.516 1659.4478_11.517 822.755_11.516
## 2         6331.303        145534.3         5374.612       287093.4
##   1653.489_11.522 1654.4923_11.523 1684.4596_11.522 1685.4637_11.522
## 2        3217.005         3502.013         7813.414         11191.29
##   951.819_11.515 848.7717_11.527 1679.5046_11.53 1680.5083_11.531
## 2       20169.02        967240.2        6822.774         7704.743
##   876.6825_11.558 877.7965_11.559 944.8656_11.558 958.8804_11.56
## 2        3346.467        122302.7          890314       398300.2
##   874.7892_11.559 874.7461_11.559 875.5569_11.56 876.854_11.56 820.6961_11.574
## 2         3172820        15439.89       2351.354       21837.2        4369.521
##   879.7408_11.578 912.8727_11.58 688.6024_11.581 900.803_11.585 970.8801_11.587
## 2        48040.11       6471.074        21189.06        1112878        325268.1
##   984.8952_11.587 905.7564_11.605 950.8167_11.564 1020.8946_11.578
## 2        149427.6        44519.53        27785.38         7168.371
##   976.8319_11.618 927.8194_11.647 862.7859_11.623 1034.911_11.594
## 2        3982.127        77872.82        222026.7        2552.789
##   955.7715_11.624 996.8931_11.636 1010.9091_11.638 888.8014_11.642
## 2        1778.225         34972.3         17250.75        190564.5
##   1416.1569_11.643 714.6184_11.643 1417.1607_11.643 893.7563_11.645
## 2          1047.74        102431.2         1195.386        23155.99
##   931.7715_11.646 926.8159_11.647 719.5732_11.65 1022.9086_11.654
## 2         15659.2        114237.9       6053.102         8046.453
##   1036.925_11.654 1366.1408_11.655 957.7865_11.655 1367.1442_11.655
## 2         4327.82         2090.963        4914.232          1783.41
##   952.831_11.66 978.8456_11.66 1316.1245_11.664 1317.1277_11.664
## 2      27333.94       2786.061         2743.785         3071.889
##   664.6011_11.668 1311.1692_11.669 1312.1724_11.668 928.8306_11.753
## 2        34742.44          3528.94         2835.189        67667.66
##   1689.4913_11.681 1688.4878_11.681 1657.5142_11.686 1683.5318_11.692
## 2          5808.51          4370.88         503.6229         5529.812
##   1684.5352_11.693 960.8944_11.707 946.8797_11.708 876.8035_11.712
## 2         6383.304        428250.2        899821.8         3226126
##   879.8107_11.711 902.8168_11.73 876.7617_11.713 878.587_11.712 972.8948_11.737
## 2        120675.1       895668.6        17487.13       2670.459        269066.1
##   902.8182_11.738 986.9104_11.739 1554.3397_11.745 1555.3432_11.744
## 2         1224359        127751.5         4530.259         3650.815
##   1041.9353_11.754 1363.2025_11.753 1364.2059_11.753 690.6188_11.754
## 2         2514.979         24606.14         25608.94        252108.4
##   673.5905_11.756 1369.1611_11.766 1368.1577_11.767 907.7722_11.767
## 2        8385.006         16062.58         14618.86        60527.93
##   928.8317_11.769 695.5729_11.772 1012.925_11.773 998.9084_11.775
## 2        67667.66        14446.94        9726.109        22197.71
##   933.7875_11.787 1339.2017_11.786 1344.1575_11.789 1345.161_11.79
## 2        11010.68         3396.515         8898.468       7879.225
##   895.7721_11.798 890.8172_11.801 1268.1254_11.807 1295.1453_11.81
## 2        39354.18        222009.8         4658.194        10834.82
##   1294.1419_11.81 1263.1707_11.815 991.9207_11.815 1289.1866_11.82
## 2         11917.9         3417.174        4366.934        12816.24
##   1290.1901_11.819 1320.1579_11.832 1321.1613_11.836 369.3517_11.844
## 2         14974.65         39232.81          39623.7        182142.1
##   1322.1646_11.831 666.619_11.852 370.3549_11.851 1298.1748_11.858
## 2         24688.49       524568.4        54212.87         6319.687
##   1299.1785_11.856 1017.9359_11.856 1318.2126_11.857 1316.2069_11.859
## 2         6578.469         25785.38         28259.07         165839.2
##   1315.2035_11.858 1341.2177_11.895 1342.2211_11.896 692.6335_11.899
## 2           159782         7678.696         8293.413        46673.77
##   693.6368_11.899 1532.3557_11.908 1346.1728_11.908 1347.1765_11.909
## 2        23759.46         3606.448         6072.426          6827.92
##   904.8336_11.91 1558.3711_11.912 988.9264_11.914 974.911_11.915
## 2       709680.4         4606.458        99856.62       219563.4
##   930.8479_11.919 1000.9255_11.924 1014.9413_11.924 1334.1724_11.947
## 2        77632.02         26061.62         12150.83         1344.198
##   956.8628_11.946 1308.157_11.948 1322.172_12.024 1350.2033_12.028
## 2        8415.506        1872.269         4146.23         2119.103
##   1324.1888_12.056 1325.1924_12.055 1299.1762_12.078 370.3549_12.075
## 2         27354.78         28560.82         15242.02        53898.55
##   1019.9516_12.075 1319.2346_12.075 1303.2104_12.075 1302.2067_12.075
## 2         54721.55         125490.6         15517.75         16427.43
##   1320.238_12.076 668.6342_12.074 1277.1948_12.078 369.3518_12.077
## 2        123558.4        166722.6         12821.56        186192.3
##   1294.2217_12.077 1268.2053_12.079 1293.2183_12.078 1326.1956_12.056
## 2         50350.77         4661.689         53581.95         15722.65
##   1267.2019_12.079 1276.1913_12.079 1250.1754_12.08 642.6107_12.077
## 2         5505.106          13411.3        4338.924        13245.47
##   1298.1727_12.089 1272.1559_12.098 1028.9572_12.137 963.8345_12.135
## 2         15940.22         2504.315         7405.451        6037.557
##   984.8939_12.149 1329.2228_12.335 1328.2189_12.336
## 2         4043.26         2327.458         2329.194

Find connections

connection <- find_connections(data = data_notame,
                               features = features,
                               corr_thresh = 0.95,
                               rt_window = 1/60,
                               name_col = "mz_rt",
                               mz_col = "mz",
                               rt_col = "rt")

## [1] 100
## [1] 200
## [1] 300
## [1] 400
## [1] 500
## [1] 600
## [1] 700
## [1] 800
## [1] 900
## [1] 1000
## [1] 1100
## [1] 1200
## [1] 1300
## [1] 1400
## [1] 1500
## [1] 1600
## [1] 1700
## [1] 1800
## [1] 1900
## [1] 2000
## [1] 2100
## [1] 2200
## [1] 2300
## [1] 2400

head(connection)

##                x              y       cor rt_diff  mz_diff
## 1 167.0891_0.631 256.1695_0.637 0.9818637   0.006  89.0804
## 2 167.0891_0.631 272.1651_0.645 0.9613305   0.014 105.0760
## 3 256.1695_0.637 272.1651_0.645 0.9517330   0.008  15.9956
## 4 265.1183_0.643 287.1003_0.648 0.9564272   0.005  21.9820
## 5 634.4366_0.676 648.4519_0.687 0.9572685   0.011  14.0153
## 6 648.4519_0.687 736.5043_0.684 0.9511110  -0.003  88.0524

Clustering

clusters <- find_clusters(connections = connection, d_thresh = 0.8)

## 242 components found
## 
## Component 100 / 242 
## Component 200 / 242 
## 55 components found
## 
## 5 components found

# assign a cluster ID to all features. Clusters are named after feature with highest median peak height
features_clustered <- assign_cluster_id(data_notame, clusters, features, name_col = "mz_rt")

# lets see how many features are removed when we only keep one feature per cluster
pulled <- pull_clusters(data_notame, features_clustered, name_col = "mz_rt")
cluster_data <- pulled$cdata
cluster_features <- pulled$cfeatures

# how much did we trim our data down by?
nrow(omicsdata) - nrow(cluster_features)

## [1] 388

# export clustered feature list
write_csv(cluster_features,
          "Notame/notame-clustered-features.csv")

Reduce dataset based on clustering

# transpose the full dataset back to wide so that it is more similar to the notame dataset
imp_meta_omics_wide <- imp_meta_omics %>%
  dplyr::select(-"row_id") %>%
  pivot_wider(names_from = mz_rt,
              values_from = peak_height)

# list of reduced features
clusternames <- cluster_features$mz_rt

# select only the features are in the reduced list
imp_clust <- imp_meta_omics_wide[,c(names(imp_meta_omics_wide) %in% clusternames)]

# bind back sample names
imp_clust <- cbind(imp_meta_omics_wide[1], imp_clust)

tibble(imp_clust)

## # A tibble: 85 × 2,038
##    sample     `189.1598_0.513` `104.1071_0.537` `184.0946_0.54` `162.1126_0.545`
##    <chr>                 <dbl>            <dbl>           <dbl>            <dbl>
##  1 x5109_b1_…            5394.          173277.          51759.          144958.
##  2 x5101_b1_…            3299.          129830.          68092.          159756.
##  3 x5109_b3_…            6079.          173316.          58650.          140823.
##  4 x5103_b3_…            4923.          161982.          53680.          140005.
##  5 x5108_b1_…            3050.          127211.          55879.          141761.
##  6 x5113_b1_…            4468.          125901.          51706.          123194.
##  7 x5112_b3_…            3506.          162774.          51842.          135057.
##  8 x5105_b1_…            7545.          168842.          55074.          144985.
##  9 x5110_b3_…            4195.          193137.          45673.          136221.
## 10 x5110_b1_…            4086.          163577.          51901.          125073 
## # ℹ 75 more rows
## # ℹ 2,033 more variables: `204.1231_0.589` <dbl>, `265.2403_0.591` <dbl>,
## #   `144.102_0.595` <dbl>, `118.0864_0.597` <dbl>, `337.0619_0.611` <dbl>,
## #   `132.102_0.612` <dbl>, `623.1355_0.614` <dbl>, `116.0707_0.623` <dbl>,
## #   `645.1222_0.627` <dbl>, `293.0984_0.628` <dbl>, `100.0757_0.629` <dbl>,
## #   `160.0606_0.629` <dbl>, `353.0342_0.629` <dbl>, `172.1333_0.62` <dbl>,
## #   `120.0808_0.631` <dbl>, `166.0861_0.629` <dbl>, `258.1105_0.632` <dbl>, …

New mz vs rt plot

Let’s see how our clustered data looks now compared to the original

# plot new rt vs mz scatterplot post-clustering
(plot_mzvsrt_postcluster <- cluster_features %>%
  ggplot(aes(x = rt,
             y = mz)) +
  geom_point() +
  theme_minimal() +
  labs(x = "Retention time, min",
       y = "m/z, neutral",
       title = "mz across RT for all features after clustering"))

# plot both scatterplots to compare with and without notame clustering
(scatterplots <- ggarrange(plot_mzvsrt, 
                           plot_mzvsrt_postcluster, 
                           nrow = 2))

Wrangle new data

# add subject column back for a seamless join with metadata
imp_clust$subject <- imp_meta_omics_wide$subject

# bind back new clustered data with metadata
imp_metabind_clust <- right_join(metadata, 
                                 imp_clust,
                                 by = "subject")

Visualize untransformed data

More wrangling

# meta data columns 
str_meta <- colnames(imp_metabind_clust[,4:13])

# change meta data columns to character so that I can change NAs from QCs to "QC"
imp_metabind_clust <- imp_metabind_clust %>%
  mutate_at(str_meta, as.character) 

# replace NAs in metadata columns for QCs
imp_metabind_clust[is.na(imp_metabind_clust)] <- "QC"

# long df
imp_metabind_clust_tidy <- imp_metabind_clust %>%
  pivot_longer(cols = 15:ncol(.),
               names_to = "mz_rt",
               values_to = "rel_abund")

# structure check
head(imp_metabind_clust_tidy)

## # A tibble: 6 × 16
##   subject treatment tomato_or_control sex   bmi   age   tot_chol ldl_chol
##   <chr>   <chr>     <chr>             <chr> <chr> <chr> <chr>    <chr>   
## 1 5107    beta      tomato            M     23.81 68    177      85.4    
## 2 5107    beta      tomato            M     23.81 68    177      85.4    
## 3 5107    beta      tomato            M     23.81 68    177      85.4    
## 4 5107    beta      tomato            M     23.81 68    177      85.4    
## 5 5107    beta      tomato            M     23.81 68    177      85.4    
## 6 5107    beta      tomato            M     23.81 68    177      85.4    
## # ℹ 8 more variables: hdl_chol <chr>, triglycerides <chr>, glucose <chr>,
## #   SBP <chr>, DBP <chr>, sample <chr>, mz_rt <chr>, rel_abund <dbl>

Boxplot

imp_metabind_clust_tidy %>%
  ggplot(aes(x = subject, y = rel_abund, color = treatment)) +
  geom_boxplot(alpha = 0.6) +
  scale_color_manual(values = c("orange", "lightgreen", "gray", "tomato1")) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90)) +
  labs(title = "LC-MS (+) Feature Abundances by Sample",
       subtitle = "Unscaled data",
       y = "Relative abundance")

Will need to log transform in order to normalize and actually see the data

Log2 transform

imp_metabind_clust_tidy_log2 <- imp_metabind_clust_tidy %>%
  mutate(rel_abund_log2 = log2(rel_abund))

Boxplot

(bp_data_quality <- imp_metabind_clust_tidy_log2 %>%
  ggplot(aes(x = sample, y = rel_abund_log2, color = treatment)) +
  geom_boxplot(alpha = 0.6) +
  scale_color_manual(values = c("orange", "lightgreen", "gray", "tomato1")) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90)) +
  labs(title = "LC-MS (+) Feature Abundances by Sample",
       subtitle = "Log2 transformed data",
       y = "Relative abundance"))

Notame drift correction

Data wrangling

Feature abund df

# filtered and imputed data after notame clustering, transposed
features_forQCcorr <- imp_clust %>%
  dplyr::select(!subject) %>%
  t() %>%
  as.data.frame() %>%
  row_to_names(row_number = "find_header")

# log2 transform
log2_features_forQCcorr <- features_forQCcorr %>%
  mutate_all(as.numeric) %>%
  log2()

# write csv to manually edit
write.csv(log2_features_forQCcorr,
          "Notame/feaures_fromR_forDC_1.csv",
          row.names = TRUE)

Import corrected df (edited so that “mz_rt” could be the row name for row 1)

log2_features_forQCcorr_new <- read.csv("Notame/feaures_forR_forDC_editedmzrt_2.csv",
                                header = FALSE,
                                row.names = 1)


log2_features_forQCcorr_new <- log2_features_forQCcorr_new %>%
  rownames_to_column(var = "mz_rt") %>%
  row_to_names(row_number = 1) %>%
  separate(col = mz_rt,
           into = c("mz", "rt"),
           sep = "_")

write.csv(log2_features_forQCcorr_new,
          "Notame/features_fromR_forDC_3.csv",
          row.names = TRUE)

Pheno df

# separate sampleID and injection order
pheno_data <- imp_clust[1] %>%
  separate(col = sample,
           into = c("sample", "injection_order"),
           # last underscore
           sep = "_(?!.*_)")



# make inj order column numeric
pheno_data <- pheno_data %>%
  mutate_at("injection_order", as.numeric)

t_pheno_data <- as.data.frame(t(pheno_data))

write.csv(t_pheno_data,
          "Notame/pheno_df.csv",
          row.names = TRUE)

Combine pheno and feature dfs manually in excel to create metaboset df.

xf## Import Metaboset

#make sure when converting csv to xlsx that you save as a new file, don't just change the name of the file
metaboset <- read_from_excel("Notame/metaboset.xlsx",
                             split_by = c("column", "Ion mode"))

## INFO [2025-10-06 20:59:15] Detecting corner position
## INFO [2025-10-06 20:59:15] Corner detected correctly at row 3, column D
## INFO [2025-10-06 20:59:15] 
## Extracting sample information from rows 1 to 3 and columns E to CK
## INFO [2025-10-06 20:59:15] Replacing spaces in sample information column names with underscores (_)
## INFO [2025-10-06 20:59:15] Naming the last column of sample information "Datafile"
## INFO [2025-10-06 20:59:15] 
## Extracting feature information from rows 4 to 2040 and columns A to D
## INFO [2025-10-06 20:59:15] Creating Split column from column, Ion mode
## INFO [2025-10-06 20:59:15] Feature_ID column not found, creating feature IDs
## INFO [2025-10-06 20:59:15] Identified m/z column mass and retention time column rt
## INFO [2025-10-06 20:59:15] Identified m/z column mass and retention time column rt
## INFO [2025-10-06 20:59:15] Creating feature IDs from Split, m/z and retention time
## INFO [2025-10-06 20:59:15] Replacing dots (.) in feature information column names with underscores (_)
## INFO [2025-10-06 20:59:15] 
## Extracting feature abundances from rows 4 to 2040 and columns E to CK
## INFO [2025-10-06 20:59:15] 
## Checking sample information
## INFO [2025-10-06 20:59:15] QC column generated from rows containing 'QC'
## INFO [2025-10-06 20:59:15] Sample ID autogenerated from injection orders and prefix ID_
## INFO [2025-10-06 20:59:15] Checking that feature abundances only contain numeric values
## INFO [2025-10-06 20:59:15] 
## Checking feature information
## INFO [2025-10-06 20:59:15] Checking that feature IDs are unique and not stored as numbers
## INFO [2025-10-06 20:59:15] Checking that m/z and retention time values are reasonable
## INFO [2025-10-06 20:59:15] Identified m/z column mass and retention time column rt
## INFO [2025-10-06 20:59:15] Identified m/z column mass and retention time column rt

#construct Metaboset
modes <- construct_metabosets(exprs = metaboset$exprs,
                              pheno_data = metaboset$pheno_data,
                              feature_data = metaboset$feature_data, group_col = "Class")

## Initializing the object(s) with unflagged features
## INFO [2025-10-06 20:59:15] 
## Checking feature information
## INFO [2025-10-06 20:59:15] Checking that feature IDs are unique and not stored as numbers
## INFO [2025-10-06 20:59:15] Checking that feature abundances only contain numeric values
## INFO [2025-10-06 20:59:15] Setting row and column names of exprs based on feature and pheno data

#extract each mode into a single object
mode <- modes$C18_pos

Boxplots before correction

# ordered by injection
(qualityBPs_b4correction <- plot_sample_boxplots(mode, order_by = c("Class", "Injection_order"), title = "Uncorrected feature abundance"))

#ordered by class
plot_sample_boxplots(mode, order_by = "Injection_order", title = "Uncorrected feature abundance")

Boxplots after QC drift correction

drift correction takes up to 2 minutes

mode <- flag_detection(mode, qc_limit = 0.75, group_limit = 0.8)

## INFO [2025-10-06 20:59:21] 
## 0% of features flagged for low detection rate

corrected <- correct_drift(mode, log_transform = FALSE)

## INFO [2025-10-06 20:59:21] 
## Starting drift correction at 2025-10-06 20:59:21.360597
## INFO [2025-10-06 20:59:28] Drift correction performed at 2025-10-06 20:59:28.162445
## INFO [2025-10-06 20:59:30] Inspecting drift correction results 2025-10-06 20:59:30.37915
## INFO [2025-10-06 20:59:35] Drift correction results inspected at 2025-10-06 20:59:35.392669
## INFO [2025-10-06 20:59:35] 
## Drift correction results inspected, report:
## Drift_corrected: 100%

Did drift correction work?

output is percent of the features that were drift corrected. The remaining “low-quality” percent represents features for which the DC did not improve the RSD and D-ratio of the original data.

inspected <- inspect_dc(orig = mode, dc = corrected, check_quality = TRUE)

## INFO [2025-10-06 20:59:37] Inspecting drift correction results 2025-10-06 20:59:37.548909
## INFO [2025-10-06 20:59:44] Drift correction results inspected at 2025-10-06 20:59:44.638607
## INFO [2025-10-06 20:59:44] 
## Drift correction results inspected, report:
## Drift_corrected: 79%,  Low_quality: 21%

Boxplots, corrected

(qualityBPS_driftcorrection <- plot_sample_boxplots(corrected, order_by = c("Class", "Injection_order"), title = "Corrected feature abundance"))

plot_sample_boxplots(corrected, order_by = "Injection_order", title = "Corrected feature abundance")

Compare quality BPs

(qualityBPs_compared <- ggarrange(qualityBPs_b4correction, qualityBPS_driftcorrection,
                    ncol = 1, nrow = 2))

Export new Metaboset to Excel spreadsheet

write_to_excel(corrected, "Notame/metaboset_corrected.xlsx")

Manually edit the df so it only has mass, rt, and sample columns

Import edited Metaboset

metabdata_corrected <- read.csv(file = "Notame/metaboset_corrected_editedforR.csv",
                                check.names = FALSE)

Wrangle new metab data

Combine mz & rt back together

metabdata_corrected_MZ_RT <- metabdata_corrected %>%
  mutate(mass = round(metabdata_corrected$mass, digits = 4), # Decrease number of decimals for m/z & rt
         rt = round(metabdata_corrected$rt, digits = 3),
         .before=1,
         .keep="unused") %>%
  unite(mz_rt, c(mass, rt), remove=TRUE) # Combine m/z & rt with _ in between

Transpose new df

metabdata_corrected_t <- as.data.frame(t(metabdata_corrected_MZ_RT)) %>%
  row_to_names(row_number = "find_header") %>% # make MZ_RT column names
  rownames_to_column(var = "sample") # change rownames to column 1

Bind new data with metadata

I want the new “metabdata_corrected_t” df to look just like “imp_metabind_clust_log2” df

# go back to wide data
imp_metabind_clust_log2 <- imp_metabind_clust_tidy_log2 %>%
  dplyr::select(!rel_abund) %>%
  pivot_wider(names_from = mz_rt,
              values_from = rel_abund_log2)

# bind metadata columns to the new drift corrected df
DC_imp_metabind_clust_log2 <- full_join(imp_metabind_clust_log2[,c(1:14)], 
                                        metabdata_corrected_t, 
                                        by = "sample")

# fix QC subject names
DC_imp_metabind_clust_log2$subject <- str_replace_all(DC_imp_metabind_clust_log2$subject, "c", "qc")

DC_imp_metabind_clust_log2 <- DC_imp_metabind_clust_log2 %>%
  mutate_at("subject", str_sub, start=1, end=4)


# make feature abundances numeric
DC_imp_metabind_clust_log2 <- DC_imp_metabind_clust_log2 %>%
  mutate_at(15:ncol(.), as.numeric)

PCAs

With QCS

Wrangle

PCA.DC_imp_metabind_clust_log2 <- PCA(DC_imp_metabind_clust_log2,  # wide data
                                   quali.sup = 1:14, # remove qualitative variables
                                   graph = FALSE, # don't graph
                                   scale.unit = FALSE) # don't scale, already transformed data

# PCA summary
kable(summary(PCA.DC_imp_metabind_clust_log2))

## 
## Call:
## PCA(X = DC_imp_metabind_clust_log2, scale.unit = FALSE, quali.sup = 1:14,  
##      graph = FALSE) 
## 
## 
## Eigenvalues
##                        Dim.1   Dim.2   Dim.3   Dim.4   Dim.5   Dim.6   Dim.7
## Variance              55.606  43.326  28.623  24.069  19.730  18.346  16.482
## % of var.             15.234  11.869   7.841   6.594   5.405   5.026   4.515
## Cumulative % of var.  15.234  27.103  34.944  41.538  46.943  51.969  56.484
##                        Dim.8   Dim.9  Dim.10  Dim.11  Dim.12  Dim.13  Dim.14
## Variance              10.320   9.932   9.033   8.458   7.767   6.841   5.992
## % of var.              2.827   2.721   2.475   2.317   2.128   1.874   1.641
## Cumulative % of var.  59.311  62.032  64.507  66.824  68.952  70.826  72.467
##                       Dim.15  Dim.16  Dim.17  Dim.18  Dim.19  Dim.20  Dim.21
## Variance               5.723   5.235   4.940   4.621   4.249   3.946   3.809
## % of var.              1.568   1.434   1.353   1.266   1.164   1.081   1.044
## Cumulative % of var.  74.035  75.469  76.822  78.088  79.253  80.334  81.377
##                       Dim.22  Dim.23  Dim.24  Dim.25  Dim.26  Dim.27  Dim.28
## Variance               3.513   3.241   3.130   2.863   2.716   2.425   2.418
## % of var.              0.962   0.888   0.858   0.784   0.744   0.664   0.662
## Cumulative % of var.  82.340  83.228  84.085  84.869  85.613  86.278  86.940
##                       Dim.29  Dim.30  Dim.31  Dim.32  Dim.33  Dim.34  Dim.35
## Variance               2.320   2.175   2.066   1.967   1.794   1.744   1.598
## % of var.              0.636   0.596   0.566   0.539   0.491   0.478   0.438
## Cumulative % of var.  87.576  88.172  88.737  89.276  89.768  90.245  90.683
##                       Dim.36  Dim.37  Dim.38  Dim.39  Dim.40  Dim.41  Dim.42
## Variance               1.556   1.448   1.383   1.331   1.303   1.226   1.193
## % of var.              0.426   0.397   0.379   0.365   0.357   0.336   0.327
## Cumulative % of var.  91.109  91.506  91.885  92.250  92.607  92.943  93.269
##                       Dim.43  Dim.44  Dim.45  Dim.46  Dim.47  Dim.48  Dim.49
## Variance               1.173   1.115   1.069   1.033   1.011   0.957   0.940
## % of var.              0.321   0.306   0.293   0.283   0.277   0.262   0.257
## Cumulative % of var.  93.591  93.896  94.189  94.472  94.749  95.011  95.269
##                       Dim.50  Dim.51  Dim.52  Dim.53  Dim.54  Dim.55  Dim.56
## Variance               0.895   0.851   0.819   0.808   0.789   0.754   0.706
## % of var.              0.245   0.233   0.224   0.221   0.216   0.207   0.193
## Cumulative % of var.  95.514  95.747  95.971  96.193  96.409  96.616  96.809
##                       Dim.57  Dim.58  Dim.59  Dim.60  Dim.61  Dim.62  Dim.63
## Variance               0.694   0.678   0.654   0.629   0.611   0.574   0.550
## % of var.              0.190   0.186   0.179   0.172   0.167   0.157   0.151
## Cumulative % of var.  96.999  97.185  97.364  97.536  97.704  97.861  98.012
##                       Dim.64  Dim.65  Dim.66  Dim.67  Dim.68  Dim.69  Dim.70
## Variance               0.540   0.513   0.493   0.490   0.470   0.463   0.452
## % of var.              0.148   0.141   0.135   0.134   0.129   0.127   0.124
## Cumulative % of var.  98.160  98.300  98.435  98.570  98.698  98.825  98.949
##                       Dim.71  Dim.72  Dim.73  Dim.74  Dim.75  Dim.76  Dim.77
## Variance               0.414   0.381   0.350   0.334   0.331   0.305   0.304
## % of var.              0.114   0.104   0.096   0.092   0.091   0.084   0.083
## Cumulative % of var.  99.063  99.167  99.263  99.355  99.445  99.529  99.612
##                       Dim.78  Dim.79  Dim.80  Dim.81  Dim.82  Dim.83  Dim.84
## Variance               0.288   0.271   0.268   0.262   0.211   0.116   0.000
## % of var.              0.079   0.074   0.073   0.072   0.058   0.032   0.000
## Cumulative % of var.  99.691  99.765  99.839  99.910  99.968 100.000 100.000
## 
## Individuals (the 10 first)
##                       Dist     Dim.1     ctr    cos2     Dim.2     ctr    cos2
## 1                |  20.874 |   0.213   0.001   0.000 |  10.940   3.250   0.275
## 2                |  27.204 | -11.312   2.707   0.173 |  13.374   4.857   0.242
## 3                |  29.175 | -20.166   8.604   0.478 | -13.918   5.260   0.228
## 4                |  24.206 | -13.883   4.078   0.329 |  -9.813   2.615   0.164
## 5                |  15.779 |  -1.225   0.032   0.006 |   0.003   0.000   0.000
## 6                |  19.091 |  -5.076   0.545   0.071 |  -6.393   1.110   0.112
## 7                |  31.853 | -23.048  11.239   0.524 | -10.881   3.215   0.117
## 8                |  18.257 |  -9.826   2.043   0.290 |  -2.831   0.218   0.024
## 9                |  18.984 |   0.289   0.002   0.000 |   4.862   0.642   0.066
## 10               |  22.033 |  -4.972   0.523   0.051 |  10.184   2.816   0.214
##                      Dim.3     ctr    cos2  
## 1                |  -0.041   0.000   0.000 |
## 2                |   0.267   0.003   0.000 |
## 3                |  -2.625   0.283   0.008 |
## 4                |   5.101   1.069   0.044 |
## 5                |   1.988   0.162   0.016 |
## 6                |   2.827   0.328   0.022 |
## 7                |  -6.916   1.966   0.047 |
## 8                |  -0.337   0.005   0.000 |
## 9                |  -1.261   0.065   0.004 |
## 10               |   1.317   0.071   0.004 |
## 
## Variables (the 10 first)
##                     Dim.1    ctr   cos2    Dim.2    ctr   cos2    Dim.3    ctr
## 189.1598_0.513   |  0.106  0.020  0.035 | -0.049  0.006  0.007 |  0.060  0.013
## 104.1071_0.537   |  0.048  0.004  0.061 | -0.010  0.000  0.003 |  0.014  0.001
## 184.0946_0.54    | -0.005  0.000  0.001 |  0.024  0.001  0.019 |  0.022  0.002
## 162.1126_0.545   |  0.028  0.001  0.030 |  0.023  0.001  0.021 |  0.013  0.001
## 204.1231_0.589   |  0.004  0.000  0.000 |  0.130  0.039  0.207 |  0.060  0.013
## 265.2403_0.591   | -0.018  0.001  0.014 | -0.005  0.000  0.001 |  0.003  0.000
## 144.102_0.595    |  0.307  0.169  0.044 |  0.076  0.013  0.003 | -0.100  0.035
## 118.0864_0.597   |  0.025  0.001  0.009 |  0.075  0.013  0.081 |  0.016  0.001
## 337.0619_0.611   |  0.000  0.000  0.000 |  0.019  0.001  0.058 |  0.015  0.001
## 132.102_0.612    |  0.022  0.001  0.008 | -0.098  0.022  0.171 | -0.013  0.001
##                    cos2  
## 189.1598_0.513    0.011 |
## 104.1071_0.537    0.005 |
## 184.0946_0.54     0.017 |
## 162.1126_0.545    0.007 |
## 204.1231_0.589    0.044 |
## 265.2403_0.591    0.000 |
## 144.102_0.595     0.005 |
## 118.0864_0.597    0.004 |
## 337.0619_0.611    0.038 |
## 132.102_0.612     0.003 |
## 
## Supplementary categories (the 10 first)
##                       Dist     Dim.1    cos2  v.test     Dim.2    cos2  v.test
## 5101             |  17.414 |  -1.630   0.009  -0.311 |   6.585   0.143   1.423
## 5102             |  14.960 |   0.172   0.000   0.033 |  -5.929   0.157  -1.282
## 5103             |  19.338 | -11.845   0.375  -2.260 |   4.470   0.053   0.966
## 5104             |  18.160 |  -8.310   0.209  -1.586 |  -8.516   0.220  -1.841
## 5105             |  16.415 |  -9.390   0.327  -1.792 |  -2.961   0.033  -0.640
## 5107             |  21.298 |  -5.550   0.068  -1.059 |  12.157   0.326   2.628
## 5108             |  21.982 | -14.027   0.407  -2.676 |   3.989   0.033   0.862
## 5109             |  24.521 | -17.024   0.482  -3.248 | -11.866   0.234  -2.565
## 5110             |  14.906 |  -8.665   0.338  -1.653 |   3.600   0.058   0.778
## 5111             |  16.322 |   6.183   0.144   1.180 |   0.501   0.001   0.108
##                      Dim.3    cos2  v.test  
## 5101             |   0.475   0.001   0.126 |
## 5102             |   1.902   0.016   0.506 |
## 5103             |  -6.347   0.108  -1.688 |
## 5104             |   1.245   0.005   0.331 |
## 5105             |   5.011   0.093   1.333 |
## 5107             |   0.113   0.000   0.030 |
## 5108             |  -2.232   0.010  -0.594 |
## 5109             |   1.238   0.003   0.329 |
## 5110             |   0.604   0.002   0.161 |
## 5111             |  -7.713   0.223  -2.051 |

		Dist		Dim.1	cos2	v.test		Dim.2	cos2	v.test		Dim.3	cos2	v.test
5101	|	17.414	|	-1.630	0.009	-0.311	|	6.585	0.143	1.423	|	0.475	0.001	0.126	|
5102	|	14.960	|	0.172	0.000	0.033	|	-5.929	0.157	-1.282	|	1.902	0.016	0.506	|
5103	|	19.338	|	-11.845	0.375	-2.260	|	4.470	0.053	0.966	|	-6.347	0.108	-1.688	|
5104	|	18.160	|	-8.310	0.209	-1.586	|	-8.516	0.220	-1.841	|	1.245	0.005	0.331	|
5105	|	16.415	|	-9.390	0.327	-1.792	|	-2.961	0.033	-0.640	|	5.011	0.093	1.333	|
5107	|	21.298	|	-5.550	0.068	-1.059	|	12.157	0.326	2.628	|	0.113	0.000	0.030	|
5108	|	21.982	|	-14.027	0.407	-2.676	|	3.989	0.033	0.862	|	-2.232	0.010	-0.594	|
5109	|	24.521	|	-17.024	0.482	-3.248	|	-11.866	0.234	-2.565	|	1.238	0.003	0.329	|
5110	|	14.906	|	-8.665	0.338	-1.653	|	3.600	0.058	0.778	|	0.604	0.002	0.161	|
5111	|	16.322	|	6.183	0.144	1.180	|	0.501	0.001	0.108	|	-7.713	0.223	-2.051	|

# pull PC coordinates into df
PC_coord_QC_log2 <- as.data.frame(PCA.DC_imp_metabind_clust_log2$ind$coord)

# bind back metadata from cols 1-14
PC_coord_QC_log2 <- bind_cols(DC_imp_metabind_clust_log2[,1:14], PC_coord_QC_log2)

# grab some variance explained
importance_QC <- PCA.DC_imp_metabind_clust_log2$eig

# set variance explained with PC1, round to 2 digits
PC1_withQC <- round(importance_QC[1,2], 2)

# set variance explained with PC2, round to 2 digits
PC2_withQC <- round(importance_QC[2,2], 2)

Plots

Using FactoExtra package

# scree plot
fviz_eig(PCA.DC_imp_metabind_clust_log2)

# get eigenvalues
kable(get_eig(PCA.DC_imp_metabind_clust_log2))

	eigenvalue	variance.percent	cumulative.variance.percent
Dim.1	55.6062932	15.2335459	15.23355
Dim.2	43.3257818	11.8692552	27.10280
Dim.3	28.6228608	7.8413366	34.94414
Dim.4	24.0687248	6.5937145	41.53785
Dim.5	19.7296407	5.4050067	46.94286
Dim.6	18.3461676	5.0259992	51.96886
Dim.7	16.4819103	4.5152791	56.48414
Dim.8	10.3197899	2.8271439	59.31128
Dim.9	9.9321320	2.7209436	62.03222
Dim.10	9.0331512	2.4746646	64.50689
Dim.11	8.4578886	2.3170693	66.82396
Dim.12	7.7665970	2.1276874	68.95165
Dim.13	6.8412777	1.8741928	70.82584
Dim.14	5.9917494	1.6414615	72.46730
Dim.15	5.7226639	1.5677445	74.03504
Dim.16	5.2347639	1.4340826	75.46913
Dim.17	4.9396562	1.3532367	76.82236
Dim.18	4.6214994	1.2660765	78.08844
Dim.19	4.2494361	1.1641484	79.25259
Dim.20	3.9460214	1.0810269	80.33362
Dim.21	3.8094183	1.0436040	81.37722
Dim.22	3.5129854	0.9623951	82.33962
Dim.23	3.2411405	0.8879222	83.22754
Dim.24	3.1304509	0.8575984	84.08514
Dim.25	2.8626531	0.7842342	84.86937
Dim.26	2.7157455	0.7439884	85.61336
Dim.27	2.4245963	0.6642270	86.27759
Dim.28	2.4181283	0.6624550	86.94004
Dim.29	2.3199122	0.6355484	87.57559
Dim.30	2.1752966	0.5959304	88.17152
Dim.31	2.0655342	0.5658606	88.73738
Dim.32	1.9665669	0.5387481	89.27613
Dim.33	1.7939804	0.4914674	89.76760
Dim.34	1.7435959	0.4776644	90.24526
Dim.35	1.5982665	0.4378509	90.68311
Dim.36	1.5562239	0.4263332	91.10944
Dim.37	1.4484581	0.3968104	91.50625
Dim.38	1.3832387	0.3789433	91.88520
Dim.39	1.3310969	0.3646588	92.24986
Dim.40	1.3032682	0.3570351	92.60689
Dim.41	1.2257640	0.3358025	92.94269
Dim.42	1.1926630	0.3267344	93.26943
Dim.43	1.1730456	0.3213601	93.59079
Dim.44	1.1153255	0.3055475	93.89634
Dim.45	1.0692251	0.2929181	94.18925
Dim.46	1.0325166	0.2828617	94.47212
Dim.47	1.0110964	0.2769935	94.74911
Dim.48	0.9566692	0.2620830	95.01119
Dim.49	0.9396273	0.2574143	95.26861
Dim.50	0.8951120	0.2452192	95.51383
Dim.51	0.8512604	0.2332059	95.74703
Dim.52	0.8190131	0.2243716	95.97140
Dim.53	0.8083077	0.2214388	96.19284
Dim.54	0.7894961	0.2162853	96.40913
Dim.55	0.7543496	0.2066568	96.61578
Dim.56	0.7058323	0.1933653	96.80915
Dim.57	0.6939990	0.1901235	96.99927
Dim.58	0.6778229	0.1856921	97.18497
Dim.59	0.6537120	0.1790868	97.36405
Dim.60	0.6288967	0.1722885	97.53634
Dim.61	0.6113481	0.1674810	97.70382
Dim.62	0.5738358	0.1572044	97.86103
Dim.63	0.5503131	0.1507603	98.01179
Dim.64	0.5396885	0.1478496	98.15964
Dim.65	0.5134275	0.1406553	98.30029
Dim.66	0.4929250	0.1350386	98.43533
Dim.67	0.4902101	0.1342948	98.56962
Dim.68	0.4701340	0.1287949	98.69842
Dim.69	0.4627609	0.1267750	98.82519
Dim.70	0.4524660	0.1239547	98.94915
Dim.71	0.4143090	0.1135014	99.06265
Dim.72	0.3813885	0.1044828	99.16713
Dim.73	0.3499514	0.0958705	99.26300
Dim.74	0.3340413	0.0915118	99.35452
Dim.75	0.3309475	0.0906643	99.44518
Dim.76	0.3054864	0.0836891	99.52887
Dim.77	0.3043578	0.0833799	99.61225
Dim.78	0.2876988	0.0788161	99.69106
Dim.79	0.2711268	0.0742762	99.76534
Dim.80	0.2680656	0.0734375	99.83878
Dim.81	0.2616145	0.0716702	99.91045
Dim.82	0.2109577	0.0577926	99.96824
Dim.83	0.1158401	0.0317348	99.99998
Dim.84	0.0000861	0.0000236	100.00000

# scores plot
fviz_pca_ind(PCA.DC_imp_metabind_clust_log2)

Manual scores plot

# manual scores plot
(PCA_withQCs <- PC_coord_QC_log2 %>%
  ggplot(aes(x = Dim.1, y = Dim.2,
             fill = factor(treatment, levels = c("control", "beta", "red", "QC")),
             text = sample)) +
  geom_point(shape = 21, alpha = 0.8) +
  scale_fill_manual(values = c("lightgreen", "orange", "tomato", "lightgray")) +
  scale_color_manual(values = "black") +  
  theme_minimal() +
  coord_fixed(PC2_withQC/PC1_withQC) +
  labs(x = glue::glue("PC1: {PC1_withQC}%"),
       y = glue::glue("PC2: {PC2_withQC}%"),
       fill = "Group",
       title = "Principal Components Analysis Scores Plot",
       subtitle = "Log2 transformed data"))

ggplotly(PCA_withQCs, tooltip = "text")

Without QCs

Wrangle

DC_imp_metabind_clust_log2_noQCs <- DC_imp_metabind_clust_log2 %>%
  filter(treatment != "QC")

PCA.DC_imp_metabind_clust_log2_noQCs <- PCA(DC_imp_metabind_clust_log2_noQCs, # wide data
                               quali.sup=1:14, # remove qualitative variables
                               graph=FALSE, # don't graph
                               scale.unit=FALSE) # don't scale, we already did this

# look at summary
kable(summary(PCA.DC_imp_metabind_clust_log2_noQCs))

## 
## Call:
## PCA(X = DC_imp_metabind_clust_log2_noQCs, scale.unit = FALSE,  
##      quali.sup = 1:14, graph = FALSE) 
## 
## 
## Eigenvalues
##                        Dim.1   Dim.2   Dim.3   Dim.4   Dim.5   Dim.6   Dim.7
## Variance              63.124  52.598  32.601  23.938  22.416  20.081  12.491
## % of var.             15.661  13.049   8.088   5.939   5.561   4.982   3.099
## Cumulative % of var.  15.661  28.710  36.798  42.737  48.299  53.281  56.380
##                        Dim.8   Dim.9  Dim.10  Dim.11  Dim.12  Dim.13  Dim.14
## Variance              12.079  11.055  10.144   9.321   8.387   7.467   6.945
## % of var.              2.997   2.743   2.517   2.313   2.081   1.853   1.723
## Cumulative % of var.  59.377  62.119  64.636  66.948  69.029  70.882  72.605
##                       Dim.15  Dim.16  Dim.17  Dim.18  Dim.19  Dim.20  Dim.21
## Variance               6.330   6.079   5.613   5.174   4.853   4.618   4.296
## % of var.              1.571   1.508   1.392   1.284   1.204   1.146   1.066
## Cumulative % of var.  74.175  75.683  77.076  78.360  79.564  80.709  81.775
##                       Dim.22  Dim.23  Dim.24  Dim.25  Dim.26  Dim.27  Dim.28
## Variance               4.010   3.782   3.479   3.295   2.939   2.900   2.825
## % of var.              0.995   0.938   0.863   0.817   0.729   0.720   0.701
## Cumulative % of var.  82.770  83.708  84.572  85.389  86.118  86.838  87.538
##                       Dim.29  Dim.30  Dim.31  Dim.32  Dim.33  Dim.34  Dim.35
## Variance               2.638   2.544   2.364   2.171   2.112   1.960   1.918
## % of var.              0.654   0.631   0.586   0.539   0.524   0.486   0.476
## Cumulative % of var.  88.193  88.824  89.411  89.949  90.473  90.959  91.435
##                       Dim.36  Dim.37  Dim.38  Dim.39  Dim.40  Dim.41  Dim.42
## Variance               1.782   1.676   1.636   1.567   1.481   1.435   1.431
## % of var.              0.442   0.416   0.406   0.389   0.367   0.356   0.355
## Cumulative % of var.  91.878  92.293  92.699  93.088  93.455  93.811  94.166
##                       Dim.43  Dim.44  Dim.45  Dim.46  Dim.47  Dim.48  Dim.49
## Variance               1.351   1.296   1.266   1.241   1.162   1.122   1.080
## % of var.              0.335   0.322   0.314   0.308   0.288   0.278   0.268
## Cumulative % of var.  94.501  94.823  95.137  95.445  95.733  96.012  96.280
##                       Dim.50  Dim.51  Dim.52  Dim.53  Dim.54  Dim.55  Dim.56
## Variance               1.026   0.985   0.985   0.943   0.931   0.856   0.844
## % of var.              0.254   0.244   0.244   0.234   0.231   0.212   0.209
## Cumulative % of var.  96.534  96.779  97.023  97.257  97.488  97.700  97.910
##                       Dim.57  Dim.58  Dim.59  Dim.60  Dim.61  Dim.62  Dim.63
## Variance               0.817   0.780   0.755   0.739   0.684   0.660   0.641
## % of var.              0.203   0.193   0.187   0.183   0.170   0.164   0.159
## Cumulative % of var.  98.112  98.306  98.493  98.677  98.846  99.010  99.169
##                       Dim.64  Dim.65  Dim.66  Dim.67  Dim.68  Dim.69
## Variance               0.600   0.594   0.562   0.556   0.537   0.500
## % of var.              0.149   0.147   0.139   0.138   0.133   0.124
## Cumulative % of var.  99.318  99.465  99.605  99.743  99.876 100.000
## 
## Individuals (the 10 first)
##                       Dist     Dim.1     ctr    cos2     Dim.2     ctr    cos2
## 1                |  20.832 |   1.319   0.039   0.004 |  10.907   3.231   0.274
## 2                |  26.942 | -10.666   2.575   0.157 |  13.458   4.919   0.250
## 3                |  28.516 | -19.057   8.219   0.447 | -13.776   5.154   0.233
## 4                |  23.768 | -13.331   4.022   0.315 |  -9.702   2.556   0.167
## 5                |  15.482 |   0.043   0.000   0.000 |  -0.031   0.000   0.000
## 6                |  18.649 |  -3.865   0.338   0.043 |  -6.393   1.110   0.118
## 7                |  31.228 | -22.079  11.032   0.500 | -10.718   3.120   0.118
## 8                |  17.890 |  -9.136   1.889   0.261 |  -2.765   0.208   0.024
## 9                |  18.776 |   1.822   0.075   0.009 |   4.810   0.628   0.066
## 10               |  21.952 |  -4.262   0.411   0.038 |  10.211   2.832   0.216
##                      Dim.3     ctr    cos2  
## 1                |   1.131   0.056   0.003 |
## 2                |  -1.834   0.147   0.005 |
## 3                |  -3.779   0.626   0.018 |
## 4                |   5.796   1.472   0.059 |
## 5                |   4.997   1.094   0.104 |
## 6                |   5.488   1.320   0.087 |
## 7                | -11.334   5.629   0.132 |
## 8                |  -1.407   0.087   0.006 |
## 9                |   1.919   0.161   0.010 |
## 10               |   1.374   0.083   0.004 |
## 
## Variables (the 10 first)
##                     Dim.1    ctr   cos2    Dim.2    ctr   cos2    Dim.3    ctr
## 189.1598_0.513   |  0.078  0.010  0.017 | -0.053  0.005  0.008 |  0.013  0.001
## 104.1071_0.537   |  0.055  0.005  0.068 | -0.012  0.000  0.003 |  0.037  0.004
## 184.0946_0.54    | -0.012  0.000  0.004 |  0.026  0.001  0.021 |  0.016  0.001
## 162.1126_0.545   |  0.033  0.002  0.034 |  0.025  0.001  0.020 |  0.028  0.002
## 204.1231_0.589   |  0.002  0.000  0.000 |  0.144  0.039  0.208 |  0.086  0.022
## 265.2403_0.591   | -0.023  0.001  0.019 | -0.006  0.000  0.001 | -0.008  0.000
## 144.102_0.595    |  0.243  0.093  0.024 |  0.084  0.014  0.003 | -0.260  0.207
## 118.0864_0.597   |  0.025  0.001  0.007 |  0.083  0.013  0.082 |  0.017  0.001
## 337.0619_0.611   | -0.007  0.000  0.007 |  0.021  0.001  0.067 |  0.004  0.000
## 132.102_0.612    |  0.022  0.001  0.007 | -0.109  0.022  0.172 | -0.022  0.001
##                    cos2  
## 189.1598_0.513    0.000 |
## 104.1071_0.537    0.030 |
## 184.0946_0.54     0.007 |
## 162.1126_0.545    0.025 |
## 204.1231_0.589    0.074 |
## 265.2403_0.591    0.003 |
## 144.102_0.595     0.028 |
## 118.0864_0.597    0.003 |
## 337.0619_0.611    0.002 |
## 132.102_0.612     0.007 |
## 
## Supplementary categories (the 10 first)
##                       Dist     Dim.1    cos2  v.test     Dim.2    cos2  v.test
## 5101             |  17.122 |  -0.213   0.000  -0.038 |   6.548   0.146   1.286
## 5102             |  14.884 |   1.148   0.006   0.206 |  -5.963   0.161  -1.171
## 5103             |  18.649 | -10.429   0.313  -1.870 |   4.525   0.059   0.889
## 5104             |  17.990 |  -8.003   0.198  -1.435 |  -8.444   0.220  -1.659
## 5105             |  16.049 |  -8.890   0.307  -1.594 |  -2.885   0.032  -0.567
## 5107             |  21.110 |  -4.674   0.049  -0.838 |  12.183   0.333   2.393
## 5108             |  21.374 | -12.876   0.363  -2.309 |   4.078   0.036   0.801
## 5109             |  23.911 | -16.194   0.459  -2.904 | -11.739   0.241  -2.306
## 5110             |  14.250 |  -7.541   0.280  -1.352 |   3.638   0.065   0.715
## 5111             |  16.413 |   7.756   0.223   1.391 |   0.379   0.001   0.074
##                      Dim.3    cos2  v.test  
## 5101             |   3.618   0.045   0.903 |
## 5102             |   4.207   0.080   1.050 |
## 5103             |  -8.042   0.186  -2.007 |
## 5104             |  -0.595   0.001  -0.149 |
## 5105             |   5.463   0.116   1.363 |
## 5107             |  -0.351   0.000  -0.088 |
## 5108             |  -2.850   0.018  -0.711 |
## 5109             |   1.008   0.002   0.252 |
## 5110             |   1.196   0.007   0.298 |
## 5111             |  -6.722   0.168  -1.677 |

		Dist		Dim.1	cos2	v.test		Dim.2	cos2	v.test		Dim.3	cos2	v.test
5101	|	17.122	|	-0.213	0.000	-0.038	|	6.548	0.146	1.286	|	3.618	0.045	0.903	|
5102	|	14.884	|	1.148	0.006	0.206	|	-5.963	0.161	-1.171	|	4.207	0.080	1.050	|
5103	|	18.649	|	-10.429	0.313	-1.870	|	4.525	0.059	0.889	|	-8.042	0.186	-2.007	|
5104	|	17.990	|	-8.003	0.198	-1.435	|	-8.444	0.220	-1.659	|	-0.595	0.001	-0.149	|
5105	|	16.049	|	-8.890	0.307	-1.594	|	-2.885	0.032	-0.567	|	5.463	0.116	1.363	|
5107	|	21.110	|	-4.674	0.049	-0.838	|	12.183	0.333	2.393	|	-0.351	0.000	-0.088	|
5108	|	21.374	|	-12.876	0.363	-2.309	|	4.078	0.036	0.801	|	-2.850	0.018	-0.711	|
5109	|	23.911	|	-16.194	0.459	-2.904	|	-11.739	0.241	-2.306	|	1.008	0.002	0.252	|
5110	|	14.250	|	-7.541	0.280	-1.352	|	3.638	0.065	0.715	|	1.196	0.007	0.298	|
5111	|	16.413	|	7.756	0.223	1.391	|	0.379	0.001	0.074	|	-6.722	0.168	-1.677	|

# pull PC coordinates into df
PC_coord_noQCs_log2 <- as.data.frame(PCA.DC_imp_metabind_clust_log2_noQCs$ind$coord)

# bind back metadata from cols 1-14
PC_coord_noQCs_log2 <- bind_cols(DC_imp_metabind_clust_log2_noQCs[,1:14], PC_coord_noQCs_log2)

# grab some variance explained
importance_noQC <- PCA.DC_imp_metabind_clust_log2_noQCs$eig

# set variance explained with PC1, round to 2 digits
PC1_noQC <- round(importance_noQC[1,2], 2)

# set variance explained with PC2, round to 2 digits
PC2_noQC <- round(importance_noQC[2,2], 2)

Plots

Using FactoExtra

# scree plot
fviz_eig(PCA.DC_imp_metabind_clust_log2_noQCs)

# scores plot
fviz_pca_ind(PCA.DC_imp_metabind_clust_log2_noQCs)

Manual scores plot

# wrangling data prior to plot for ease
PC_coord_noQCs_log2 <- PC_coord_noQCs_log2 %>%
  mutate(sample2 = sample) %>%
  mutate_at("sample2", str_sub, start=7, end=8) %>%
  mutate(period = sample2) %>%
  unite(treatment_period, "treatment", "period", sep = "_", remove = FALSE) %>%
  dplyr::select(!sample2) %>%
  mutate_at("period", as.factor) %>%
  # relevel factors
  mutate(treatment_period = fct_relevel(treatment_period, c("control_b1", "control_b3", 
                                                            "beta_b1", "beta_b3",
                                                            "red_b1", "red_b3")),
         treatment = fct_relevel(treatment, c("control", "beta", "red")))

(PCA_withoutQCs <- PC_coord_noQCs_log2 %>%
   ggplot(aes(x = Dim.1, y = Dim.2,
             fill = treatment_period,
             text = sample)) +
   geom_point(shape = 21, alpha = 0.8) +
   geom_hline(yintercept = 0, linetype = "dashed", alpha=0.5) +
   geom_vline(xintercept = 0, linetype = "dashed", alpha=0.5) +
  scale_fill_manual(values = c("darkseagreen2", "darkgreen", 
                               "tan3", "orangered2",
                               "lavenderblush3", "darkred")) +
  scale_color_manual(values = "black") +  
  theme_minimal() +
  coord_fixed(PC2_noQC/PC1_noQC) +
  labs(x = glue::glue("PC1: {PC1_noQC}%"),
       y = glue::glue("PC2: {PC2_noQC}%"),
       fill = "Group",
       title = "Principal Components Analysis Scores Plot",
       subtitle = "Log2 transformed data, No QCs"))

ggplotly(PCA_withoutQCs, tooltip = "text")

Faceted by period

(PCA_faceted_noQCs <- PC_coord_noQCs_log2 %>%
   ggplot(aes(x = Dim.1, y = Dim.2,
             fill = treatment_period,
             text = sample)) +
   geom_point(shape = 21, alpha = 0.8) +
   geom_hline(yintercept = 0, linetype = "dashed", alpha=0.5) +
   geom_vline(xintercept = 0, linetype = "dashed", alpha=0.5) +
  scale_fill_manual(values = c("darkseagreen2", "darkgreen", 
                               "tan3", "orangered2",
                               "lavenderblush3", "darkred")) +
  scale_color_manual(values = "black") +  
  theme_bw() +
  coord_fixed(PC2_noQC/PC1_noQC) +
  labs(x = glue::glue("PC1: {PC1_noQC}%"),
       y = glue::glue("PC2: {PC2_noQC}%"),
       fill = "treatment_period",
       title = "Principal Components Analysis Scores Plot",
       subtitle = "Log2 transformed data, No QCs") +
  facet_wrap( ~ period) +
   theme(strip.background = element_rect(fill="white")))

ggplotly(PCA_faceted_noQCs, tooltip = "text")

PC_coord_noQCs_log2 %>%
   ggplot(aes(x = Dim.1, y = Dim.2,
             fill = sex,
             text = sample)) +
   geom_point(shape = 21, alpha = 0.8) +
   geom_hline(yintercept = 0, linetype = "dashed", alpha=0.5) +
   geom_vline(xintercept = 0, linetype = "dashed", alpha=0.5) +
  scale_fill_manual(values = c("purple", "blue")) +
  scale_color_manual(values = "black") +  
  theme_bw() +
  coord_fixed(PC2_noQC/PC1_noQC) +
  labs(x = glue::glue("PC1: {PC1_noQC}%"),
       y = glue::glue("PC2: {PC2_noQC}%"),
       fill = "sex",
       title = "Principal Components Analysis Scores Plot",
       subtitle = "Log2 transformed data, No QCs") +
  facet_wrap( ~ period) +
   theme(strip.background = element_rect(fill="white"))

Faceted by trt

(PCA_faceted_noQCs2 <- PC_coord_noQCs_log2 %>%
   ggplot(aes(x = Dim.1, y = Dim.2,
             fill = treatment_period,
             text = sample)) +
   geom_point(shape = 21, alpha = 0.8) +
   geom_hline(yintercept = 0, linetype = "dashed", alpha=0.5) +
   geom_vline(xintercept = 0, linetype = "dashed", alpha=0.5) +
  scale_fill_manual(values = c("darkseagreen2", "darkgreen", 
                               "tan", "orangered2",
                               "lavenderblush3", "darkred"),
                    labels = c("pre control", "post control",
                               "pre beta", "post beta",
                               "pre lyc", "post lyc")) +
  scale_color_manual(values = "black") +  
  theme_bw() +
  coord_fixed(PC2_noQC/PC1_noQC) +
  labs(x = glue::glue("PC1: {PC1_noQC}%"),
       y = glue::glue("PC2: {PC2_noQC}%"),
       fill = "Intervention timepoint",
       title = "Principal Components Analysis Scores Plot",
       subtitle = "Log2 transformed data faceted by treatment group ") +
   facet_wrap( ~ treatment))

ggplotly(PCA_faceted_noQCs2, tooltip = "text")

Export plot

ggsave(plot = PCA_faceted_noQCs2,
       filename = "plots and figures/PCA-faceted-by-group-lipidomicsPOS.svg",
       bg = "transparent",
       height = 3,
       width = 8)

PC_coord_noQCs_log2 %>%
   ggplot(aes(x = Dim.1, y = Dim.2,
             fill = sex,
             text = sample)) +
   geom_point(shape = 21, alpha = 0.8) +
   geom_hline(yintercept = 0, linetype = "dashed", alpha=0.5) +
   geom_vline(xintercept = 0, linetype = "dashed", alpha=0.5) +
  scale_fill_manual(values = c("purple", "blue")) +
  scale_color_manual(values = "black") +  
  theme_bw() +
  coord_fixed(PC2_noQC/PC1_noQC) +
  labs(x = glue::glue("PC1: {PC1_noQC}%"),
       y = glue::glue("PC2: {PC2_noQC}%"),
       fill = "sex",
       title = "Principal Components Analysis Scores Plot",
       subtitle = "Log2 transformed data, No QCs") +
  facet_wrap( ~ treatment) +
   theme(strip.background = element_rect(fill="white"))

Faceted by sex

(PC_coord_facetsex <- PC_coord_noQCs_log2 %>%
   ggplot(aes(x = Dim.1, y = Dim.2,
             fill = treatment_period,
             text = sample)) +
   geom_point(shape = 21, alpha = 0.8) +
   geom_hline(yintercept = 0, linetype = "dashed", alpha=0.5) +
   geom_vline(xintercept = 0, linetype = "dashed", alpha=0.5) +
  scale_fill_manual(values = c("darkseagreen2", "darkgreen", 
                               "tan3", "orangered2",
                               "lavenderblush3", "darkred")) +
  scale_color_manual(values = "black") +  
  theme_bw() +
  coord_fixed(PC2_noQC/PC1_noQC) +
  labs(x = glue::glue("PC1: {PC1_noQC}%"),
       y = glue::glue("PC2: {PC2_noQC}%"),
       fill = "sex",
       title = "Principal Components Analysis Scores Plot",
       subtitle = "Log2 transformed data, No QCs") +
  facet_wrap( ~ sex) +
   theme(strip.background = element_rect(fill="white")))

ggplotly(PC_coord_facetsex, tooltip = "text")

Multilevel PCA

This type of PCA accounts for the structure of paired data, allowing for a more accurate assessment of biological differences between treatment groups, not differences attributed to the natural variation between individuals.

See http://mixomics.org/methods/multilevel/ for more info.

Wrangle

Data_forMPCA <- DC_imp_metabind_clust_log2_noQCs %>%
  mutate_at("subject", as.factor) %>%
  mutate(sample2 = sample,
         b_c = carotenoids$b_c_nmol_l_plasma,
         lyc = carotenoids$lyc_nmol_l_plasma,
         apo13one = carotenoids$apo13one_nmol_l_plasma,
         retinol = carotenoids$retinol_nmol_l_plasma,
         total_carot = carotenoids$total_carotenoids) %>%
  mutate_at("sample2", str_sub, start=7, end=8) %>%
  mutate(period = sample2) %>%
  dplyr::select(c(1:14), period, b_c, lyc, apo13one, retinol, total_carot, everything()) %>%
  dplyr::select(!sample2)
 

summary(as.factor(Data_forMPCA$subject))

## 5101 5102 5103 5104 5105 5107 5108 5109 5110 5111 5112 5113 5114 5115 5116 5117 
##    2    2    2    2    2    2    2    2    2    2    2    2    2    2    2    2 
## 5118 5119 5120 5121 5122 5123 5124 5125 5126 5127 5128 5129 5130 5131 5132 5133 
##    2    2    2    2    2    2    2    2    2    2    2    2    2    2    2    2 
## 5134 5135 5136 
##    2    2    2

# make a vector for meta variables
(metavar <- Data_forMPCA[,c(1:20)] %>%
    colnames())

##  [1] "subject"           "treatment"         "tomato_or_control"
##  [4] "sex"               "bmi"               "age"              
##  [7] "tot_chol"          "ldl_chol"          "hdl_chol"         
## [10] "triglycerides"     "glucose"           "SBP"              
## [13] "DBP"               "sample"            "period"           
## [16] "b_c"               "lyc"               "apo13one"         
## [19] "retinol"           "total_carot"

Regular PCA

mixOmicsPCA.result <- mixOmics::pca(Data_forMPCA[,!names(Data_forMPCA) %in% metavar],
                            scale = FALSE,
                            center = FALSE)

plotIndiv(mixOmicsPCA.result, 
          ind.names = Data_forMPCA$subject, 
          group = Data_forMPCA$treatment, 
          legend = TRUE, 
          legend.title = "Treatment", 
          title = 'Regular PCA, Lipidomics C18 (+)')

Multilevel PCA

With all data

multilevelPCA.result <- mixOmics::pca(Data_forMPCA[,-(c(1:20))], 
                            multilevel = Data_forMPCA$subject,
                            scale = FALSE,
                            center = FALSE,ncomp = 10)

plotIndiv(multilevelPCA.result, 
          ind.names = Data_forMPCA$period, 
          group = Data_forMPCA$treatment, 
          legend = TRUE, 
          legend.title = "Treatment", 
          title = 'Multilevel PCA, Lipidomics C18 (+)',comp = c(1,2))

Loadings

plotLoadings(multilevelPCA.result, ndisplay = 30)

PCAtools

Data wrangling

# create rel abund df suitable for PCAtools package
imp_clust_omicsdata_forPCAtools <- Data_forMPCA %>%
  # select only sample ID and feature columns
  dplyr::select(sample,
                21:ncol(.)) %>%
  # transpose
  t() %>%
  # convert back to df
  as.data.frame()

names(imp_clust_omicsdata_forPCAtools) <- imp_clust_omicsdata_forPCAtools[1,] # make samp;e IDs column names

imp_clust_omicsdata_forPCAtools <- imp_clust_omicsdata_forPCAtools[-1,] # remove sample ID row

# create metadata df suitable for PCAtools pckg

metadata_forPCAtools <- Data_forMPCA[,1:20]

metadata_forPCAtools <- metadata_forPCAtools %>%
  unite("treatment_period", c("treatment", "period"), sep = "_", remove = FALSE) %>%
  column_to_rownames("sample")
  

# create a vector so that col names in abundance df matches metadata df
order_forPCAtools <- match(colnames(imp_clust_omicsdata_forPCAtools), rownames(metadata_forPCAtools))

# reorder col names in abundance df so that it matches metadata
log2_abundances_reordered_forPCAtools <- imp_clust_omicsdata_forPCAtools[,order_forPCAtools] %>%
  # change abundance df to numeric
  mutate_all(as.numeric)

PCA

colby trt

# pca
p <- PCAtools::pca(log2_abundances_reordered_forPCAtools,
                   metadata = metadata_forPCAtools,
                   scale = FALSE, # using scaled data already (log2 transformed)
         
)

biplot(p, 
       lab = paste0(metadata_forPCAtools$subject),
       colby = 'treatment',
       colkey = c("control" = "lightgreen",
                  "beta" = "orange",
                  "red" = "tomato"),
       hline = 0, vline = 0,
       legendPosition = 'right',
       title = "PCA Scores Plot with Loadings",
       subtitle = "Log2 transformed data. 95% CI ellipses",
       ellipse = TRUE,
       ellipseType = 't', # assumes multivariate
       ellipseLevel = 0.95,
       ellipseFill = TRUE,
       ellipseAlpha = 0.2,
       ellipseLineSize = 0,
       showLoadings = TRUE,ntopLoadings = 10)

colby period

# pca

biplot(p,
       lab = paste0(metadata_forPCAtools$subject),
       colby = 'period',
       colkey = c("b1" = "gray",
                  "b3" = "pink"),
       hline = 0, vline = 0,
       legendPosition = 'right',
       title = "PCA Scores Plot with Loadings",
       subtitle = "Log2 transformed data. 95% CI ellipses",
       ellipse = TRUE,
       ellipseType = 't', # assumes multivariate
       ellipseLevel = 0.95,
       ellipseFill = TRUE,
       ellipseAlpha = 0.2,
       ellipseLineSize = 0,
       showLoadings = TRUE)

colby trt_period

biplot(p,
       lab = paste0(metadata_forPCAtools$subject),
       colby = 'treatment_period',
       colkey = c("control_b1" = "darkseagreen2",
               "control_b3" = "darkgreen",
               "beta_b1" = "tan3",
               "beta_b3" = "orangered2",
               "red_b1" = "lavenderblush3",
               "red_b3" = "darkred"),
       hline = 0, vline = 0,
       legendPosition = 'right',
       title = "PCA Scores Plot with Loadings",
       subtitle = "Log2 transformed data. 95% CI ellipses.",
       ellipse = TRUE,
       ellipseType = 't', # assumes multivariate
       ellipseLevel = 0.95,
       ellipseFill = TRUE,
       ellipseAlpha = 0.2,
       ellipseLineSize = 0,
       showLoadings = TRUE, ntopLoadings = 10)

biplot(p,
       lab = paste0(metadata_forPCAtools$subject),
       colby = 'treatment_period',
       colkey = c("control_b1" = "darkseagreen2",
               "control_b3" = "darkgreen",
               "beta_b1" = "tan3",
               "beta_b3" = "orangered2",
               "red_b1" = "lavenderblush3",
               "red_b3" = "darkred"),
       hline = 0, vline = 0,
       legendPosition = 'right',
       title = "PCA Scores Plot with Loadings",
       subtitle = "Log2 transformed data. 95% CI ellipses.",
       showLoadings = TRUE, ntopLoadings = 10)

Let’s explore a little more

How many PCs do we need to capture at least 80% variance?

which(cumsum(p$variance) > 80)[1]

## PC20 
##   20

This shows we’d need quite a few PCs to capture most of the variance.

Pairs plots

Here, we will look at separations for several components at once using pairs plots.

treatment_period

pairsplot(p,
    components = getComponents(p, c(1:10)),
    triangle = TRUE, trianglelabSize = 12,
    hline = 0, vline = 0,
    pointSize = 0.4,
    gridlines.major = FALSE, gridlines.minor = FALSE,
    colby = 'treatment_period',
    colkey = c("control_b1" = "darkseagreen2",
               "control_b3" = "darkgreen",
               "beta_b1" = "tan",
               "beta_b3" = "orangered2",
               "red_b1" = "lavenderblush3",
               "red_b3" = "darkred"),
    title = 'Pairs plot', plotaxes = FALSE,
    margingaps = unit(c(-0.01, -0.01, -0.01, -0.01), 'cm'))

period

pairsplot(p,
    components = getComponents(p, c(1:10)),
    triangle = TRUE, trianglelabSize = 12,
    hline = 0, vline = 0,
    pointSize = 0.4,
    gridlines.major = FALSE, gridlines.minor = FALSE,
    colby = 'period',
    colkey = c("b1" = "darkgray",
               "b3" = "pink"),
    title = 'Pairs plot', plotaxes = FALSE,
    margingaps = unit(c(-0.01, -0.01, -0.01, -0.01), 'cm'))

sex

Are there any obvious clusterings when colored by sex?

  pairsplot(p,
    components = getComponents(p, c(1:10)),
    triangle = TRUE, trianglelabSize = 12,
    hline = 0, vline = 0,
    pointSize = 0.4,
    gridlines.major = FALSE, gridlines.minor = FALSE,
    colby = 'sex', 
    colkey = c("M" = "red",
               "F" = "purple"),
    title = 'Pairs plot', plotaxes = FALSE,
    margingaps = unit(c(-0.01, -0.01, -0.01, -0.01), 'cm'))

Eigen corplots

This is a cool way to explore the correlations between the metadata and the PCs! I want to look at how the metavariables correlate with PCs that account for 80% variation in the dataset.

Again: How many PCs do we need to capture at least 80% variance?

which(cumsum(p$variance) > 80)[1]

## PC20 
##   20

  eigencorplot(p,
    components = getComponents(p, 1:which(cumsum(p$variance) > 80)[1]), # get components that account for 80% variance
    metavars = colnames(metadata_forPCAtools),
    col = c('darkblue', 'blue2', 'gray', 'red2', 'darkred'),
    cexCorval = 0.7,
    colCorval = 'white',
    fontCorval = 2,
    posLab = 'bottomleft',
    rotLabX = 45,
    posColKey = 'top',
    cexLabColKey = 1.5,
    scale = TRUE,
    main = 'PC1-15 metadata correlations',
    colFrame = 'white',
    plotRsquared = FALSE)

  eigencorplot(p,
    components = getComponents(p, 1:which(cumsum(p$variance) > 80)[1]),
    metavars = colnames(metadata_forPCAtools),
    col = c('white', 'cornsilk1', 'gold', 'forestgreen', 'darkgreen'),
    cexCorval = 1.2,
    fontCorval = 2,
    posLab = 'all',
    rotLabX = 45,
    scale = TRUE,
    main = bquote(Principal ~ component ~ Spearman ~ r^2 ~ metadata ~ correlates),
    plotRsquared = TRUE,
    corFUN = 'spearman',
    corUSE = 'pairwise.complete.obs',
    corMultipleTestCorrection = 'BH',
    signifSymbols = c('****', '***', '**', '*', ''),
    signifCutpoints = c(0, 0.0001, 0.001, 0.01, 0.05, 1))

Univariate analysis

Wrangle data

# using the most convenient df to convert to tidy
df_for_stats <- Data_forMPCA %>%
  pivot_longer(21:ncol(.),
               names_to = "mz_rt",
               values_to = "rel_abund_log2") %>%
  unite("treatment_period", "treatment", "period", sep = "_", remove = FALSE) %>%
  mutate_at("treatment", as.factor)

# turn off sci notation outputs
options(scipen = 999)

Parametric tests

ANOVA across treatment timepoints

df_for_stats$treatment_period <- as.factor(df_for_stats$treatment_period)

trt_anova_output_df <- df_for_stats %>% 
  dplyr::select(subject, sample, treatment_period, mz_rt, rel_abund_log2) %>%
  group_by(mz_rt) %>%
  anova_test(rel_abund_log2 ~ treatment_period, wid = subject,
             detailed = TRUE) %>%
  adjust_pvalue(method = "BH") %>%
  as.data.frame()


trt_anova_sig <- trt_anova_output_df %>%
  filter(p.adj < .05)

head(trt_anova_sig)

##             mz_rt           Effect   SSn    SSd DFn DFd     F         p p<.05
## 1  118.0864_0.597 treatment_period 1.548  4.336   5  64 4.571 0.0010000     *
## 2  1341.0272_6.83 treatment_period 6.978 16.964   5  64 5.265 0.0004120     *
## 3  1363.015_6.835 treatment_period 6.298 15.876   5  64 5.078 0.0005550     *
## 4 1365.0287_6.836 treatment_period 6.375 17.802   5  64 4.584 0.0010000     *
## 5 1531.6527_7.711 treatment_period 2.010  5.072   5  64 5.073 0.0005590     *
## 6 1585.2008_8.971 treatment_period 2.329  4.069   5  64 7.326 0.0000176     *
##     ges      p.adj
## 1 0.263 0.03772222
## 2 0.291 0.03496850
## 3 0.284 0.03772222
## 4 0.264 0.03772222
## 5 0.284 0.03772222
## 6 0.364 0.00298760

# how many significant features?
nrow(trt_anova_sig)

## [1] 54

trt_period_ANOVA_tukey <- df_for_stats %>% 
  dplyr::select(subject, sample, treatment_period, mz_rt, rel_abund_log2) %>%
  group_by(mz_rt) %>%
  tukey_hsd(rel_abund_log2 ~ treatment_period, wid = subject)

trt_tukeyHSD_sig <- trt_period_ANOVA_tukey %>%
  filter(p.adj < .05)

t tests (unpaired and paired)

Paired control

# run paired t-tests for control intervention
ctrl_t.test_paired <- df_for_stats %>%
  filter(treatment == "control") %>%
 dplyr::select(subject, period, mz_rt, rel_abund_log2) %>%
  group_by(mz_rt) %>%
  t_test(rel_abund_log2 ~ period, 
         paired = TRUE, 
         p.adjust.method = "BH") %>% # Benjamini-Hochberg controlling to lower false positives
  add_significance()

Statistically significant features

# which features are significant?
ctrl_t.test_paired_sig <- ctrl_t.test_paired %>%
  filter(p < 0.05)
kable(ctrl_t.test_paired_sig)

mz_rt	.y.	group1	group2	n1	n2	statistic	df	p	p.signif
1017.6873_3.095	rel_abund_log2	b1	b3	11	11	2.790664	10	0.0191000	*
1020.8931_11.508	rel_abund_log2	b1	b3	11	11	-3.297219	10	0.0080500	**
1026.8485_11.128	rel_abund_log2	b1	b3	11	11	-2.807671	10	0.0185000	*
1028.9572_12.137	rel_abund_log2	b1	b3	11	11	2.256003	10	0.0477000	*
1034.9093_11.511	rel_abund_log2	b1	b3	11	11	-2.930130	10	0.0150000	*
1034.911_11.594	rel_abund_log2	b1	b3	11	11	-3.354362	10	0.0073100	**
1039.6688_3.106	rel_abund_log2	b1	b3	11	11	3.105943	10	0.0111000	*
104.1071_0.537	rel_abund_log2	b1	b3	11	11	-3.874240	10	0.0030900	**
1041.9353_11.754	rel_abund_log2	b1	b3	11	11	-2.387721	10	0.0381000	*
1065.848_6.509	rel_abund_log2	b1	b3	11	11	-2.476544	10	0.0327000	*
1083.7892_6.33	rel_abund_log2	b1	b3	11	11	-2.626526	10	0.0253000	*
1151.3639_7.711	rel_abund_log2	b1	b3	11	11	3.766235	10	0.0036800	**
1151.8656_7.71	rel_abund_log2	b1	b3	11	11	7.499461	10	0.0000206	****
118.0864_0.597	rel_abund_log2	b1	b3	11	11	-8.101096	10	0.0000105	****
1192.8412_6.709	rel_abund_log2	b1	b3	11	11	-2.454356	10	0.0340000	*
1193.3431_6.71	rel_abund_log2	b1	b3	11	11	-2.969116	10	0.0141000	*
1193.9116_8.867	rel_abund_log2	b1	b3	11	11	2.399487	10	0.0373000	*
1204.4035_8.865	rel_abund_log2	b1	b3	11	11	3.000891	10	0.0133000	*
1204.9948_9.897	rel_abund_log2	b1	b3	11	11	2.434276	10	0.0352000	*
1205.4054_8.863	rel_abund_log2	b1	b3	11	11	3.643491	10	0.0045100	**
1205.4975_9.898	rel_abund_log2	b1	b3	11	11	3.151023	10	0.0103000	*
1211.3587_7.312	rel_abund_log2	b1	b3	11	11	2.477648	10	0.0327000	*
1216.4896_9.892	rel_abund_log2	b1	b3	11	11	2.927260	10	0.0151000	*
1226.4736_8.96	rel_abund_log2	b1	b3	11	11	-2.308458	10	0.0436000	*
1227.3893_7.956	rel_abund_log2	b1	b3	11	11	-2.532025	10	0.0298000	*
1227.7565_5.834	rel_abund_log2	b1	b3	11	11	-2.885803	10	0.0162000	*
1231.5117_9.905	rel_abund_log2	b1	b3	11	11	-2.335208	10	0.0417000	*
1237.8134_10.084	rel_abund_log2	b1	b3	11	11	2.496035	10	0.0317000	*
1242.5051_10.22	rel_abund_log2	b1	b3	11	11	3.050200	10	0.0122000	*
1242.5053_9.906	rel_abund_log2	b1	b3	11	11	-2.634050	10	0.0250000	*
1251.8293_10.466	rel_abund_log2	b1	b3	11	11	-2.704581	10	0.0221000	*
1259.7951_10.087	rel_abund_log2	b1	b3	11	11	6.820917	10	0.0000462	****
1263.8289_10.349	rel_abund_log2	b1	b3	11	11	3.117356	10	0.0109000	*
1267.2019_12.079	rel_abund_log2	b1	b3	11	11	-2.236068	10	0.0493000	*
1272.1559_12.098	rel_abund_log2	b1	b3	11	11	-3.901447	10	0.0029500	**
1297.1947_10.828	rel_abund_log2	b1	b3	11	11	2.293987	10	0.0447000	*
1298.1727_12.089	rel_abund_log2	b1	b3	11	11	-3.151109	10	0.0103000	*
1298.1983_10.827	rel_abund_log2	b1	b3	11	11	4.440455	10	0.0012500	**
1299.1762_12.078	rel_abund_log2	b1	b3	11	11	-3.977143	10	0.0026100	**
1302.2067_12.075	rel_abund_log2	b1	b3	11	11	2.820547	10	0.0181000	*
1322.2624_10.832	rel_abund_log2	b1	b3	11	11	4.326599	10	0.0015000	**
1324.1888_12.056	rel_abund_log2	b1	b3	11	11	2.726371	10	0.0213000	*
1329.2228_12.335	rel_abund_log2	b1	b3	11	11	-2.942960	10	0.0147000	*
1339.0138_6.829	rel_abund_log2	b1	b3	11	11	2.468721	10	0.0332000	*
1341.0272_6.83	rel_abund_log2	b1	b3	11	11	3.998976	10	0.0025200	**
1347.1765_11.909	rel_abund_log2	b1	b3	11	11	-3.203456	10	0.0094400	**
1363.015_6.835	rel_abund_log2	b1	b3	11	11	4.108668	10	0.0021200	**
1365.0287_6.836	rel_abund_log2	b1	b3	11	11	7.045136	10	0.0000352	****
1367.1442_11.655	rel_abund_log2	b1	b3	11	11	-2.290500	10	0.0450000	*
1369.1611_11.766	rel_abund_log2	b1	b3	11	11	-2.373578	10	0.0390000	*
1389.0311_6.813	rel_abund_log2	b1	b3	11	11	3.670679	10	0.0043100	**
1444.1182_6.522	rel_abund_log2	b1	b3	11	11	2.773396	10	0.0197000	*
1458.1721_6.714	rel_abund_log2	b1	b3	11	11	-2.865090	10	0.0168000	*
1470.0283_7.811	rel_abund_log2	b1	b3	11	11	-2.397023	10	0.0375000	*
1473.1717_7.517	rel_abund_log2	b1	b3	11	11	-2.362144	10	0.0398000	*
1475.1518_7.004	rel_abund_log2	b1	b3	11	11	-4.267897	10	0.0016400	**
1476.1552_7.005	rel_abund_log2	b1	b3	11	11	-4.931828	10	0.0005940	***
1481.1402_7.792	rel_abund_log2	b1	b3	11	11	-2.857176	10	0.0170000	*
1482.1063_6.531	rel_abund_log2	b1	b3	11	11	3.656134	10	0.0044200	**
1486.0818_6.746	rel_abund_log2	b1	b3	11	11	2.377445	10	0.0388000	*
1487.1515_6.871	rel_abund_log2	b1	b3	11	11	-2.352899	10	0.0404000	*
1497.1674_7.486	rel_abund_log2	b1	b3	11	11	-2.974166	10	0.0139000	*
1504.14_8.066	rel_abund_log2	b1	b3	11	11	2.677861	10	0.0232000	*
1509.1328_6.475	rel_abund_log2	b1	b3	11	11	2.294904	10	0.0446000	*
1511.145_6.854	rel_abund_log2	b1	b3	11	11	-2.638300	10	0.0248000	*
1517.1985_7.759	rel_abund_log2	b1	b3	11	11	2.351366	10	0.0405000	*
1520.1651_7.717	rel_abund_log2	b1	b3	11	11	5.047982	10	0.0005010	***
1522.0815_7.299	rel_abund_log2	b1	b3	11	11	2.273527	10	0.0463000	*
1523.1825_7.536	rel_abund_log2	b1	b3	11	11	-2.354399	10	0.0403000	*
1524.1325_6.944	rel_abund_log2	b1	b3	11	11	3.091632	10	0.0114000	*
1528.1659_7.665	rel_abund_log2	b1	b3	11	11	2.710389	10	0.0219000	*
1530.1768_7.745	rel_abund_log2	b1	b3	11	11	2.770219	10	0.0198000	*
1531.1683_7.726	rel_abund_log2	b1	b3	11	11	2.528654	10	0.0299000	*
1531.181_7.771	rel_abund_log2	b1	b3	11	11	3.118148	10	0.0109000	*
1531.2124_8.09	rel_abund_log2	b1	b3	11	11	-5.631046	10	0.0002180	***
1531.6527_7.711	rel_abund_log2	b1	b3	11	11	5.681205	10	0.0002040	***
1532.1582_7.713	rel_abund_log2	b1	b3	11	11	4.160777	10	0.0019500	**
1535.1521_6.713	rel_abund_log2	b1	b3	11	11	-3.478310	10	0.0059400	**
1542.1448_7.703	rel_abund_log2	b1	b3	11	11	4.657994	10	0.0008970	***
1542.645_7.698	rel_abund_log2	b1	b3	11	11	7.110448	10	0.0000325	****
1543.1481_7.702	rel_abund_log2	b1	b3	11	11	3.306853	10	0.0079200	**
1543.2104_7.94	rel_abund_log2	b1	b3	11	11	-2.750715	10	0.0205000	*
1544.1549_7.686	rel_abund_log2	b1	b3	11	11	2.314713	10	0.0432000	*
1546.1749_7.78	rel_abund_log2	b1	b3	11	11	3.755782	10	0.0037500	**
1546.1772_8.02	rel_abund_log2	b1	b3	11	11	3.737425	10	0.0038600	**
1547.1793_7.789	rel_abund_log2	b1	b3	11	11	2.361596	10	0.0398000	*
1547.1803_7.856	rel_abund_log2	b1	b3	11	11	2.317510	10	0.0430000	*
1547.245_8.769	rel_abund_log2	b1	b3	11	11	2.606359	10	0.0262000	*
1548.1302_6.87	rel_abund_log2	b1	b3	11	11	2.439971	10	0.0349000	*
1548.6249_6.858	rel_abund_log2	b1	b3	11	11	-4.408658	10	0.0013200	**
1550.1462_7.021	rel_abund_log2	b1	b3	11	11	2.740372	10	0.0208000	*
1550.14_6.908	rel_abund_log2	b1	b3	11	11	3.143230	10	0.0105000	*
1553.1308_7.035	rel_abund_log2	b1	b3	11	11	-3.042733	10	0.0124000	*
1554.1833_7.701	rel_abund_log2	b1	b3	11	11	2.720627	10	0.0215000	*
1557.1328_6.71	rel_abund_log2	b1	b3	11	11	-3.039261	10	0.0125000	*
1558.1201_7.783	rel_abund_log2	b1	b3	11	11	-2.985060	10	0.0137000	*
1558.1357_6.713	rel_abund_log2	b1	b3	11	11	-3.291355	10	0.0081300	**
1560.0999_6.725	rel_abund_log2	b1	b3	11	11	3.098703	10	0.0113000	*
1562.1488_7.418	rel_abund_log2	b1	b3	11	11	-2.708787	10	0.0220000	*
1562.1493_7.418	rel_abund_log2	b1	b3	11	11	-3.050176	10	0.0122000	*
1563.1166_6.865	rel_abund_log2	b1	b3	11	11	2.610726	10	0.0260000	*
1563.1535_7.418	rel_abund_log2	b1	b3	11	11	-3.184553	10	0.0097400	**
1563.2185_8.966	rel_abund_log2	b1	b3	11	11	-2.733878	10	0.0211000	*
1565.1377_6.892	rel_abund_log2	b1	b3	11	11	2.421521	10	0.0360000	*
1570.1778_7.936	rel_abund_log2	b1	b3	11	11	2.381044	10	0.0385000	*
1572.1304_7.394	rel_abund_log2	b1	b3	11	11	-2.767814	10	0.0199000	*
1574.2638_8.923	rel_abund_log2	b1	b3	11	11	3.471699	10	0.0060000	**
1577.1237_6.867	rel_abund_log2	b1	b3	11	11	-2.437085	10	0.0350000	*
1577.6354_7.106	rel_abund_log2	b1	b3	11	11	3.015619	10	0.0130000	*
1580.1032_7.792	rel_abund_log2	b1	b3	11	11	-2.874781	10	0.0165000	*
1585.2008_8.971	rel_abund_log2	b1	b3	11	11	-2.920967	10	0.0153000	*
1586.2043_8.972	rel_abund_log2	b1	b3	11	11	-3.476796	10	0.0059500	**
1591.2114_7.764	rel_abund_log2	b1	b3	11	11	-2.903396	10	0.0157000	*
1592.2118_7.764	rel_abund_log2	b1	b3	11	11	-3.383741	10	0.0069600	**
1595.6827_8.019	rel_abund_log2	b1	b3	11	11	2.238139	10	0.0492000	*
1596.3093_9.895	rel_abund_log2	b1	b3	11	11	2.519878	10	0.0304000	*
1596.6247_6.715	rel_abund_log2	b1	b3	11	11	-2.776857	10	0.0196000	*
1597.1201_6.716	rel_abund_log2	b1	b3	11	11	-4.018448	10	0.0024400	**
1597.3131_9.894	rel_abund_log2	b1	b3	11	11	2.621334	10	0.0255000	*
1598.1451_7.197	rel_abund_log2	b1	b3	11	11	-5.554611	10	0.0002430	***
1604.1937_7.739	rel_abund_log2	b1	b3	11	11	-3.230493	10	0.0090100	**
1606.677_8.009	rel_abund_log2	b1	b3	11	11	2.450950	10	0.0342000	*
1616.16_7.754	rel_abund_log2	b1	b3	11	11	-2.691308	10	0.0226000	*
162.1126_0.545	rel_abund_log2	b1	b3	11	11	-2.932879	10	0.0150000	*
1621.1991_7.959	rel_abund_log2	b1	b3	11	11	-3.630060	10	0.0046100	**
1621.2757_7.96	rel_abund_log2	b1	b3	11	11	-2.323910	10	0.0425000	*
1622.2784_7.959	rel_abund_log2	b1	b3	11	11	-3.131680	10	0.0107000	*
1623.3297_9.9	rel_abund_log2	b1	b3	11	11	3.244637	10	0.0088000	**
1624.3343_9.899	rel_abund_log2	b1	b3	11	11	2.393566	10	0.0377000	*
1624.3414_10.412	rel_abund_log2	b1	b3	11	11	-3.394402	10	0.0068400	**
1629.2009_7.725	rel_abund_log2	b1	b3	11	11	-3.251763	10	0.0086900	**
1631.1866_7.959	rel_abund_log2	b1	b3	11	11	-2.271844	10	0.0464000	*
1647.2105_8.001	rel_abund_log2	b1	b3	11	11	-2.333932	10	0.0418000	*
1648.3413_9.902	rel_abund_log2	b1	b3	11	11	-3.045530	10	0.0123000	*
1649.3444_10.225	rel_abund_log2	b1	b3	11	11	3.337395	10	0.0075200	**
1685.4637_11.522	rel_abund_log2	b1	b3	11	11	2.606833	10	0.0262000	*
1689.4913_11.681	rel_abund_log2	b1	b3	11	11	2.555455	10	0.0286000	*
1693.3899_10.581	rel_abund_log2	b1	b3	11	11	2.289356	10	0.0451000	*
205.1221_0.621	rel_abund_log2	b1	b3	11	11	5.036155	10	0.0005090	***
286.1437_1.13	rel_abund_log2	b1	b3	11	11	-3.184792	10	0.0097400	**
293.0984_0.628	rel_abund_log2	b1	b3	11	11	3.089539	10	0.0115000	*
308.1855_0.633	rel_abund_log2	b1	b3	11	11	-13.931210	10	0.0000001	****
310.2012_0.633	rel_abund_log2	b1	b3	11	11	-2.698826	10	0.0224000	*
312.1597_1.413	rel_abund_log2	b1	b3	11	11	-7.879904	10	0.0000134	****
331.0535_0.634	rel_abund_log2	b1	b3	11	11	3.365045	10	0.0071800	**
373.2483_1.147	rel_abund_log2	b1	b3	11	11	-3.117361	10	0.0109000	*
383.1533_0.931	rel_abund_log2	b1	b3	11	11	2.296307	10	0.0445000	*
398.3413_0.639	rel_abund_log2	b1	b3	11	11	-2.925002	10	0.0152000	*
424.3418_1.822	rel_abund_log2	b1	b3	11	11	-2.594059	10	0.0268000	*
426.3213_1.384	rel_abund_log2	b1	b3	11	11	2.974343	10	0.0139000	*
428.3732_2.759	rel_abund_log2	b1	b3	11	11	3.119610	10	0.0109000	*
464.1914_0.656	rel_abund_log2	b1	b3	11	11	-3.096723	10	0.0113000	*
480.3446_3.381	rel_abund_log2	b1	b3	11	11	-2.772086	10	0.0197000	*
482.3601_3.403	rel_abund_log2	b1	b3	11	11	-2.232306	10	0.0496000	*
502.2902_3.355	rel_abund_log2	b1	b3	11	11	2.782971	10	0.0194000	*
518.4564_5.944	rel_abund_log2	b1	b3	11	11	-4.686718	10	0.0008590	***
521.4196_4.236	rel_abund_log2	b1	b3	11	11	2.290164	10	0.0450000	*
522.3564_3.24	rel_abund_log2	b1	b3	11	11	2.561755	10	0.0283000	*
526.2644_1.714	rel_abund_log2	b1	b3	11	11	-6.125405	10	0.0001120	***
536.437_10.971	rel_abund_log2	b1	b3	11	11	13.947401	10	0.0000001	****
536.437_11.201	rel_abund_log2	b1	b3	11	11	3.880596	10	0.0030600	**
538.3859_4.051	rel_abund_log2	b1	b3	11	11	-3.764525	10	0.0036900	**
542.3237_2.234	rel_abund_log2	b1	b3	11	11	2.344564	10	0.0410000	*
544.3374_3.238	rel_abund_log2	b1	b3	11	11	3.485266	10	0.0058700	**
552.4018_4.301	rel_abund_log2	b1	b3	11	11	2.556762	10	0.0285000	*
568.4266_4.64	rel_abund_log2	b1	b3	11	11	-3.679443	10	0.0042500	**
577.5185_8.358	rel_abund_log2	b1	b3	11	11	4.566190	10	0.0010300	**
593.4757_4.54	rel_abund_log2	b1	b3	11	11	6.666969	10	0.0000559	****
593.4762_4.644	rel_abund_log2	b1	b3	11	11	3.776202	10	0.0036200	**
593.4768_4.312	rel_abund_log2	b1	b3	11	11	3.861204	10	0.0031500	**
595.4929_4.645	rel_abund_log2	b1	b3	11	11	14.235717	10	0.0000001	****
596.4154_4.906	rel_abund_log2	b1	b3	11	11	-7.086453	10	0.0000335	****
597.5086_5.242	rel_abund_log2	b1	b3	11	11	4.854831	10	0.0006670	***
602.5849_10.522	rel_abund_log2	b1	b3	11	11	2.292973	10	0.0448000	*
603.2923_2.783	rel_abund_log2	b1	b3	11	11	2.259576	10	0.0474000	*
610.431_5.16	rel_abund_log2	b1	b3	11	11	-2.740268	10	0.0208000	*
617.4748_4.643	rel_abund_log2	b1	b3	11	11	4.438551	10	0.0012600	**
619.4903_4.953	rel_abund_log2	b1	b3	11	11	9.332923	10	0.0000030	****
622.6132_10.675	rel_abund_log2	b1	b3	11	11	3.892456	10	0.0030000	**
623.1355_0.614	rel_abund_log2	b1	b3	11	11	2.704836	10	0.0221000	*
634.5399_9.061	rel_abund_log2	b1	b3	11	11	-2.604696	10	0.0263000	*
634.54_8.941	rel_abund_log2	b1	b3	11	11	-2.417715	10	0.0362000	*
645.1222_0.627	rel_abund_log2	b1	b3	11	11	3.328483	10	0.0076400	**
650.6449_10.83	rel_abund_log2	b1	b3	11	11	3.864561	10	0.0031400	**
652.6596_10.839	rel_abund_log2	b1	b3	11	11	3.511019	10	0.0056200	**
661.5282_5.288	rel_abund_log2	b1	b3	11	11	-2.403092	10	0.0371000	*
664.5997_4.075	rel_abund_log2	b1	b3	11	11	-2.440161	10	0.0348000	*
682.5516_8.574	rel_abund_log2	b1	b3	11	11	-2.275984	10	0.0461000	*
691.5742_6.403	rel_abund_log2	b1	b3	11	11	-3.254538	10	0.0086500	**
692.5581_7.191	rel_abund_log2	b1	b3	11	11	-2.898210	10	0.0159000	*
692.6335_11.899	rel_abund_log2	b1	b3	11	11	-2.994384	10	0.0135000	*
693.6368_11.899	rel_abund_log2	b1	b3	11	11	-2.589768	10	0.0270000	*
705.5909_6.851	rel_abund_log2	b1	b3	11	11	-2.621539	10	0.0255000	*
714.6184_11.643	rel_abund_log2	b1	b3	11	11	-2.511412	10	0.0308000	*
715.5745_6.252	rel_abund_log2	b1	b3	11	11	-2.448506	10	0.0343000	*
717.5903_6.828	rel_abund_log2	b1	b3	11	11	-3.294410	10	0.0080900	**
717.5903_7.018	rel_abund_log2	b1	b3	11	11	-4.808502	10	0.0007140	***
718.5742_8.138	rel_abund_log2	b1	b3	11	11	-2.660488	10	0.0239000	*
718.5934_7.015	rel_abund_log2	b1	b3	11	11	-4.251334	10	0.0016900	**
719.5732_11.65	rel_abund_log2	b1	b3	11	11	-2.352399	10	0.0405000	*
719.6006_7.392	rel_abund_log2	b1	b3	11	11	-5.923367	10	0.0001460	***
724.5272_7.284	rel_abund_log2	b1	b3	11	11	2.623312	10	0.0255000	*
727.5745_6.067	rel_abund_log2	b1	b3	11	11	-4.444692	10	0.0012500	**
729.5912_6.714	rel_abund_log2	b1	b3	11	11	-3.055669	10	0.0121000	*
731.6047_7.112	rel_abund_log2	b1	b3	11	11	-2.493482	10	0.0318000	*
731.6073_7.511	rel_abund_log2	b1	b3	11	11	-3.013350	10	0.0130000	*
732.5542_6.723	rel_abund_log2	b1	b3	11	11	2.253349	10	0.0479000	*
732.589_8.53	rel_abund_log2	b1	b3	11	11	-2.807446	10	0.0186000	*
733.6215_7.929	rel_abund_log2	b1	b3	11	11	-2.723050	10	0.0214000	*
738.5064_6.603	rel_abund_log2	b1	b3	11	11	2.670328	10	0.0235000	*
740.5222_7.221	rel_abund_log2	b1	b3	11	11	2.822173	10	0.0181000	*
742.5718_8.28	rel_abund_log2	b1	b3	11	11	-4.024335	10	0.0024200	**
743.6054_7.227	rel_abund_log2	b1	b3	11	11	-4.783727	10	0.0007420	***
745.6202_7.852	rel_abund_log2	b1	b3	11	11	-3.479042	10	0.0059300	**
745.6214_8.115	rel_abund_log2	b1	b3	11	11	-6.101679	10	0.0001150	***
746.5685_9.239	rel_abund_log2	b1	b3	11	11	2.742176	10	0.0208000	*
749.5557_6.066	rel_abund_log2	b1	b3	11	11	-5.420586	10	0.0002930	***
750.6771_10.977	rel_abund_log2	b1	b3	11	11	2.278371	10	0.0459000	*
751.5717_6.812	rel_abund_log2	b1	b3	11	11	-2.887073	10	0.0162000	*
755.6054_6.838	rel_abund_log2	b1	b3	11	11	-5.183933	10	0.0004110	***
756.5512_7.537	rel_abund_log2	b1	b3	11	11	3.198349	10	0.0095200	**
757.6221_7.776	rel_abund_log2	b1	b3	11	11	-2.959948	10	0.0143000	*
759.1094_6.983	rel_abund_log2	b1	b3	11	11	2.648287	10	0.0244000	*
759.4259_6.982	rel_abund_log2	b1	b3	11	11	4.281369	10	0.0016100	**
759.6382_8.714	rel_abund_log2	b1	b3	11	11	2.288984	10	0.0451000	*
760.3659_6.98	rel_abund_log2	b1	b3	11	11	-2.589948	10	0.0270000	*
760.421_6.983	rel_abund_log2	b1	b3	11	11	2.554147	10	0.0287000	*
760.5329_6.983	rel_abund_log2	b1	b3	11	11	3.560542	10	0.0051800	**
760.5459_7.719	rel_abund_log2	b1	b3	11	11	2.958440	10	0.0143000	*
760.5883_7.719	rel_abund_log2	b1	b3	11	11	4.712476	10	0.0008260	***
760.6287_6.982	rel_abund_log2	b1	b3	11	11	2.976224	10	0.0139000	*
760.9763_7.718	rel_abund_log2	b1	b3	11	11	3.502954	10	0.0057000	**
761.0061_6.983	rel_abund_log2	b1	b3	11	11	-4.238070	10	0.0017200	**
761.0763_7.717	rel_abund_log2	b1	b3	11	11	2.992336	10	0.0135000	*
761.1927_7.717	rel_abund_log2	b1	b3	11	11	3.539643	10	0.0053600	**
761.2595_7.716	rel_abund_log2	b1	b3	11	11	5.223183	10	0.0003880	***
761.302_7.716	rel_abund_log2	b1	b3	11	11	5.360447	10	0.0003190	***
761.3174_7.717	rel_abund_log2	b1	b3	11	11	5.078632	10	0.0004790	***
761.329_7.717	rel_abund_log2	b1	b3	11	11	6.217180	10	0.0000992	****
761.3681_7.718	rel_abund_log2	b1	b3	11	11	5.018328	10	0.0005230	***
761.3781_7.717	rel_abund_log2	b1	b3	11	11	4.794810	10	0.0007290	***
761.4062_7.717	rel_abund_log2	b1	b3	11	11	6.627743	10	0.0000587	****
761.4141_7.718	rel_abund_log2	b1	b3	11	11	5.649868	10	0.0002120	***
761.4437_7.717	rel_abund_log2	b1	b3	11	11	6.621500	10	0.0000592	****
761.4545_7.719	rel_abund_log2	b1	b3	11	11	5.358206	10	0.0003200	***
761.5191_7.716	rel_abund_log2	b1	b3	11	11	4.030493	10	0.0024000	**
762.5032_7.719	rel_abund_log2	b1	b3	11	11	3.259102	10	0.0085900	**
762.5038_7.219	rel_abund_log2	b1	b3	11	11	2.879223	10	0.0164000	*
762.5251_7.716	rel_abund_log2	b1	b3	11	11	3.259952	10	0.0085700	**
762.541_6.576	rel_abund_log2	b1	b3	11	11	3.495720	10	0.0057700	**
762.5433_7.717	rel_abund_log2	b1	b3	11	11	2.368750	10	0.0394000	*
762.6465_8.683	rel_abund_log2	b1	b3	11	11	2.350880	10	0.0406000	*
766.5356_8.411	rel_abund_log2	b1	b3	11	11	2.549588	10	0.0289000	*
766.5755_7.417	rel_abund_log2	b1	b3	11	11	-3.199563	10	0.0095000	**
768.551_7.206	rel_abund_log2	b1	b3	11	11	2.495888	10	0.0317000	*
768.5536_6.434	rel_abund_log2	b1	b3	11	11	-2.480081	10	0.0325000	*
771.5762_7.712	rel_abund_log2	b1	b3	11	11	4.255860	10	0.0016700	**
771.6366_8.357	rel_abund_log2	b1	b3	11	11	-3.578324	10	0.0050300	**
773.6516_9.025	rel_abund_log2	b1	b3	11	11	-3.329204	10	0.0076300	**
775.6661_9.814	rel_abund_log2	b1	b3	11	11	-3.508378	10	0.0056500	**
776.579_6.552	rel_abund_log2	b1	b3	11	11	2.626572	10	0.0253000	*
780.5549_6.325	rel_abund_log2	b1	b3	11	11	2.325567	10	0.0424000	*
780.5921_7.79	rel_abund_log2	b1	b3	11	11	-3.286691	10	0.0082000	**
781.6192_8.718	rel_abund_log2	b1	b3	11	11	2.601895	10	0.0264000	*
781.62_7.043	rel_abund_log2	b1	b3	11	11	-4.151504	10	0.0019800	**
783.431_6.876	rel_abund_log2	b1	b3	11	11	-2.269323	10	0.0466000	*
783.6368_7.847	rel_abund_log2	b1	b3	11	11	-5.106310	10	0.0004600	***
784.3942_6.875	rel_abund_log2	b1	b3	11	11	-2.257312	10	0.0476000	*
784.5042_6.876	rel_abund_log2	b1	b3	11	11	-2.228794	10	0.0499000	*
784.6654_10.38	rel_abund_log2	b1	b3	11	11	4.009412	10	0.0024800	**
785.6536_8.747	rel_abund_log2	b1	b3	11	11	-3.185869	10	0.0097200	**
785.6542_8.953	rel_abund_log2	b1	b3	11	11	3.264839	10	0.0085000	**
786.998_8.053	rel_abund_log2	b1	b3	11	11	2.412384	10	0.0365000	*
787.6674_9.202	rel_abund_log2	b1	b3	11	11	-3.021612	10	0.0129000	*
787.6701_9.896	rel_abund_log2	b1	b3	11	11	3.748403	10	0.0037900	**
788.5563_7.415	rel_abund_log2	b1	b3	11	11	-2.899328	10	0.0159000	*
788.6177_8.867	rel_abund_log2	b1	b3	11	11	2.582580	10	0.0273000	*
790.5744_7.193	rel_abund_log2	b1	b3	11	11	-2.809908	10	0.0185000	*
791.5433_6.743	rel_abund_log2	b1	b3	11	11	2.659951	10	0.0239000	*
792.5531_6.261	rel_abund_log2	b1	b3	11	11	-2.688357	10	0.0228000	*
792.5893_7.058	rel_abund_log2	b1	b3	11	11	2.248018	10	0.0483000	*
796.5845_7.27	rel_abund_log2	b1	b3	11	11	-3.512298	10	0.0056100	**
796.5848_7.423	rel_abund_log2	b1	b3	11	11	-2.862426	10	0.0169000	*
796.621_8.383	rel_abund_log2	b1	b3	11	11	-2.653310	10	0.0242000	*
797.6383_8.603	rel_abund_log2	b1	b3	11	11	-4.600854	10	0.0009790	***
797.6421_8.39	rel_abund_log2	b1	b3	11	11	-2.800757	10	0.0188000	*
798.6518_8.391	rel_abund_log2	b1	b3	11	11	-5.073527	10	0.0004820	***
798.681_10.534	rel_abund_log2	b1	b3	11	11	-4.335600	10	0.0014800	**
799.6686_9.32	rel_abund_log2	b1	b3	11	11	-3.020639	10	0.0129000	*
799.6692_9.585	rel_abund_log2	b1	b3	11	11	-4.003415	10	0.0025000	**
800.5726_6.827	rel_abund_log2	b1	b3	11	11	3.403573	10	0.0067300	**
801.686_10.409	rel_abund_log2	b1	b3	11	11	-2.793656	10	0.0190000	*
802.5357_6.323	rel_abund_log2	b1	b3	11	11	2.295529	10	0.0446000	*
802.5717_7.81	rel_abund_log2	b1	b3	11	11	-3.456271	10	0.0061600	**
803.037_6.321	rel_abund_log2	b1	b3	11	11	2.425888	10	0.0357000	*
805.0108_3.238	rel_abund_log2	b1	b3	11	11	3.626300	10	0.0046400	**
805.6187_7.848	rel_abund_log2	b1	b3	11	11	-4.209535	10	0.0018000	**
805.6784_9.291	rel_abund_log2	b1	b3	11	11	2.656918	10	0.0240000	*
806.6235_6.835	rel_abund_log2	b1	b3	11	11	-2.473973	10	0.0329000	*
807.0682_7.274	rel_abund_log2	b1	b3	11	11	-2.439077	10	0.0349000	*
807.6349_8.948	rel_abund_log2	b1	b3	11	11	3.291612	10	0.0081300	**
807.6359_7.483	rel_abund_log2	b1	b3	11	11	-3.199096	10	0.0095100	**
808.6375_8.751	rel_abund_log2	b1	b3	11	11	-3.140074	10	0.0105000	*
809.6507_9.876	rel_abund_log2	b1	b3	11	11	3.694318	10	0.0041500	**
809.6525_8.065	rel_abund_log2	b1	b3	11	11	-4.376990	10	0.0013800	**
810.5988_8.865	rel_abund_log2	b1	b3	11	11	2.293539	10	0.0447000	*
810.6041_7.959	rel_abund_log2	b1	b3	11	11	-2.312996	10	0.0433000	*
810.6564_8.03	rel_abund_log2	b1	b3	11	11	-4.195689	10	0.0018400	**
811.2977_7.959	rel_abund_log2	b1	b3	11	11	-4.232538	10	0.0017400	**
811.3445_7.959	rel_abund_log2	b1	b3	11	11	-2.769423	10	0.0198000	*
811.4149_7.961	rel_abund_log2	b1	b3	11	11	-4.200459	10	0.0018300	**
811.5374_7.96	rel_abund_log2	b1	b3	11	11	-5.134960	10	0.0004410	***
811.6292_7.819	rel_abund_log2	b1	b3	11	11	-2.652600	10	0.0242000	*
811.6703_8.961	rel_abund_log2	b1	b3	11	11	-2.860995	10	0.0169000	*
812.615_8.136	rel_abund_log2	b1	b3	11	11	2.307248	10	0.0437000	*
812.6971_10.626	rel_abund_log2	b1	b3	11	11	2.366738	10	0.0395000	*
813.6834_9.437	rel_abund_log2	b1	b3	11	11	-4.294580	10	0.0015700	**
813.6854_10.226	rel_abund_log2	b1	b3	11	11	2.982937	10	0.0137000	*
814.535_6.26	rel_abund_log2	b1	b3	11	11	-4.494118	10	0.0011500	**
814.6127_7.96	rel_abund_log2	b1	b3	11	11	-3.593260	10	0.0049000	**
815.7025_10.568	rel_abund_log2	b1	b3	11	11	3.543498	10	0.0053300	**
816.6467_9.969	rel_abund_log2	b1	b3	11	11	2.887736	10	0.0162000	*
817.7051_10.344	rel_abund_log2	b1	b3	11	11	-2.522684	10	0.0302000	*
818.5668_7.42	rel_abund_log2	b1	b3	11	11	-2.442891	10	0.0347000	*
818.6049_7.793	rel_abund_log2	b1	b3	11	11	-4.737023	10	0.0007960	***
819.6577_6.728	rel_abund_log2	b1	b3	11	11	2.281530	10	0.0457000	*
820.5846_7.213	rel_abund_log2	b1	b3	11	11	-2.626086	10	0.0253000	*
820.6202_7.907	rel_abund_log2	b1	b3	11	11	-6.894889	10	0.0000422	****
822.5994_7.509	rel_abund_log2	b1	b3	11	11	-2.824003	10	0.0180000	*
823.6664_10.409	rel_abund_log2	b1	b3	11	11	-3.399214	10	0.0067800	**
824.616_8.544	rel_abund_log2	b1	b3	11	11	-4.965452	10	0.0005650	***
824.6527_9.167	rel_abund_log2	b1	b3	11	11	-3.411620	10	0.0066400	**
824.6537_9.596	rel_abund_log2	b1	b3	11	11	-2.426975	10	0.0356000	*
825.6241_9.886	rel_abund_log2	b1	b3	11	11	2.891676	10	0.0161000	*
825.6778_9.575	rel_abund_log2	b1	b3	11	11	-5.083266	10	0.0004760	***
825.6815_9.335	rel_abund_log2	b1	b3	11	11	-8.902253	10	0.0000046	****
825.682_9.561	rel_abund_log2	b1	b3	11	11	-3.640444	10	0.0045300	**
826.5348_5.993	rel_abund_log2	b1	b3	11	11	-3.550238	10	0.0052700	**
826.5716_8.868	rel_abund_log2	b1	b3	11	11	2.317315	10	0.0430000	*
827.6071_6.849	rel_abund_log2	b1	b3	11	11	-2.787202	10	0.0192000	*
827.663_8.483	rel_abund_log2	b1	b3	11	11	-2.240679	10	0.0489000	*
827.6998_10.277	rel_abund_log2	b1	b3	11	11	-4.529116	10	0.0010900	**
827.6999_10.502	rel_abund_log2	b1	b3	11	11	-5.432917	10	0.0002880	***
828.702_10.497	rel_abund_log2	b1	b3	11	11	-3.287469	10	0.0081800	**
829.7155_10.657	rel_abund_log2	b1	b3	11	11	-3.604212	10	0.0048100	**
831.57_7.208	rel_abund_log2	b1	b3	11	11	-2.387458	10	0.0381000	*
831.6353_8.196	rel_abund_log2	b1	b3	11	11	-5.504651	10	0.0002600	***
832.582_7.631	rel_abund_log2	b1	b3	11	11	-2.325999	10	0.0423000	*
833.6507_8.971	rel_abund_log2	b1	b3	11	11	-2.880051	10	0.0164000	*
834.5932_5.658	rel_abund_log2	b1	b3	11	11	-3.097365	10	0.0113000	*
834.5933_5.658	rel_abund_log2	b1	b3	11	11	-3.293658	10	0.0081000	**
834.6002_7.257	rel_abund_log2	b1	b3	11	11	-6.538562	10	0.0000657	****
834.602_7.751	rel_abund_log2	b1	b3	11	11	-3.182630	10	0.0097800	**
835.655_7.749	rel_abund_log2	b1	b3	11	11	-3.122365	10	0.0108000	*
835.6664_10.22	rel_abund_log2	b1	b3	11	11	3.068750	10	0.0119000	*
835.6666_9.91	rel_abund_log2	b1	b3	11	11	-2.569816	10	0.0279000	*
837.6724_9.906	rel_abund_log2	b1	b3	11	11	-2.764084	10	0.0200000	*
837.6789_10.324	rel_abund_log2	b1	b3	11	11	-3.089231	10	0.0115000	*
837.6831_9.217	rel_abund_log2	b1	b3	11	11	-3.054443	10	0.0122000	*
838.6301_9.147	rel_abund_log2	b1	b3	11	11	2.964374	10	0.0142000	*
844.6193_8.384	rel_abund_log2	b1	b3	11	11	-2.477625	10	0.0327000	*
846.6352_8.854	rel_abund_log2	b1	b3	11	11	-3.679591	10	0.0042500	**
847.6893_7.716	rel_abund_log2	b1	b3	11	11	3.725516	10	0.0039400	**
848.6521_9.028	rel_abund_log2	b1	b3	11	11	-6.282026	10	0.0000912	****
849.6682_6.746	rel_abund_log2	b1	b3	11	11	2.658591	10	0.0240000	*
851.6839_7.537	rel_abund_log2	b1	b3	11	11	2.769686	10	0.0198000	*
854.574_8.362	rel_abund_log2	b1	b3	11	11	2.500453	10	0.0314000	*
855.6216_6.322	rel_abund_log2	b1	b3	11	11	2.557677	10	0.0285000	*
856.5819_7.24	rel_abund_log2	b1	b3	11	11	-4.469703	10	0.0012000	**
856.5823_7.778	rel_abund_log2	b1	b3	11	11	-3.396172	10	0.0068100	**
858.5914_7.792	rel_abund_log2	b1	b3	11	11	-3.754446	10	0.0037600	**
858.5977_8.453	rel_abund_log2	b1	b3	11	11	-3.149130	10	0.0103000	*
858.5992_7.06	rel_abund_log2	b1	b3	11	11	-2.822393	10	0.0181000	*
859.53_8.35	rel_abund_log2	b1	b3	11	11	4.296554	10	0.0015700	**
860.4936_6.313	rel_abund_log2	b1	b3	11	11	3.811892	10	0.0034200	**
861.7048_7.713	rel_abund_log2	b1	b3	11	11	5.095519	10	0.0004670	***
863.6838_8.874	rel_abund_log2	b1	b3	11	11	2.821197	10	0.0181000	*
863.6839_6.733	rel_abund_log2	b1	b3	11	11	2.536398	10	0.0295000	*
863.7092_11.343	rel_abund_log2	b1	b3	11	11	-2.606059	10	0.0262000	*
863.7199_8.448	rel_abund_log2	b1	b3	11	11	2.913824	10	0.0155000	*
868.7389_11.19	rel_abund_log2	b1	b3	11	11	-2.279421	10	0.0458000	*
870.5225_6.318	rel_abund_log2	b1	b3	11	11	3.323319	10	0.0077000	**
870.6336_9.039	rel_abund_log2	b1	b3	11	11	-4.506489	10	0.0011300	**
870.7558_11.296	rel_abund_log2	b1	b3	11	11	-2.521124	10	0.0303000	*
872.6526_8.477	rel_abund_log2	b1	b3	11	11	-2.811585	10	0.0184000	*
872.652_8.777	rel_abund_log2	b1	b3	11	11	-3.687152	10	0.0042000	**
872.7731_11.418	rel_abund_log2	b1	b3	11	11	-2.376073	10	0.0389000	*
874.6655_10.042	rel_abund_log2	b1	b3	11	11	-2.985573	10	0.0137000	*
875.7095_11.292	rel_abund_log2	b1	b3	11	11	-2.451841	10	0.0342000	*
876.6835_10.245	rel_abund_log2	b1	b3	11	11	-4.999232	10	0.0005380	***
877.6374_9.86	rel_abund_log2	b1	b3	11	11	2.668621	10	0.0235000	*
877.7_7.713	rel_abund_log2	b1	b3	11	11	3.319495	10	0.0077500	**
878.7033_7.717	rel_abund_log2	b1	b3	11	11	3.679006	10	0.0042500	**
880.718_11.248	rel_abund_log2	b1	b3	11	11	-4.021233	10	0.0024300	**
883.6892_7.419	rel_abund_log2	b1	b3	11	11	-2.759815	10	0.0201000	*
884.7697_11.358	rel_abund_log2	b1	b3	11	11	-2.456251	10	0.0339000	*
887.7206_8.05	rel_abund_log2	b1	b3	11	11	2.288188	10	0.0452000	*
889.7358_8.865	rel_abund_log2	b1	b3	11	11	2.986168	10	0.0137000	*
891.7156_7.718	rel_abund_log2	b1	b3	11	11	4.878718	10	0.0006430	***
891.7406_11.489	rel_abund_log2	b1	b3	11	11	-2.966121	10	0.0141000	*
894.7548_11.21	rel_abund_log2	b1	b3	11	11	-3.140741	10	0.0105000	*
896.5379_6.373	rel_abund_log2	b1	b3	11	11	-2.377184	10	0.0388000	*
896.7714_11.316	rel_abund_log2	b1	b3	11	11	-2.831107	10	0.0178000	*
897.7047_7.419	rel_abund_log2	b1	b3	11	11	-2.537577	10	0.0295000	*
898.7872_11.44	rel_abund_log2	b1	b3	11	11	-2.337460	10	0.0415000	*
899.7091_11.203	rel_abund_log2	b1	b3	11	11	-3.819009	10	0.0033800	**
900.6836_9.96	rel_abund_log2	b1	b3	11	11	-3.090495	10	0.0114000	*
901.7251_11.318	rel_abund_log2	b1	b3	11	11	-2.927174	10	0.0151000	*
902.5743_7.805	rel_abund_log2	b1	b3	11	11	-3.921663	10	0.0028600	**
902.7673_8.957	rel_abund_log2	b1	b3	11	11	2.516477	10	0.0306000	*
903.6531_9.918	rel_abund_log2	b1	b3	11	11	-2.632260	10	0.0251000	*
903.7406_11.457	rel_abund_log2	b1	b3	11	11	-2.810060	10	0.0185000	*
904.7835_9.894	rel_abund_log2	b1	b3	11	11	5.601563	10	0.0002270	***
909.5461_8.55	rel_abund_log2	b1	b3	11	11	2.525515	10	0.0301000	*
912.8727_11.58	rel_abund_log2	b1	b3	11	11	2.774995	10	0.0196000	*
916.7378_11.078	rel_abund_log2	b1	b3	11	11	-2.575547	10	0.0276000	*
918.754_11.178	rel_abund_log2	b1	b3	11	11	-3.183675	10	0.0097600	**
918.7987_9.897	rel_abund_log2	b1	b3	11	11	2.246482	10	0.0485000	*
920.7705_11.302	rel_abund_log2	b1	b3	11	11	-3.875040	10	0.0030800	**
922.7862_11.386	rel_abund_log2	b1	b3	11	11	-3.230804	10	0.0090100	**
923.7092_11.174	rel_abund_log2	b1	b3	11	11	-3.209320	10	0.0093400	**
928.7831_8.964	rel_abund_log2	b1	b3	11	11	-2.606576	10	0.0262000	*
930.7988_10.228	rel_abund_log2	b1	b3	11	11	2.356292	10	0.0402000	*
931.7715_11.646	rel_abund_log2	b1	b3	11	11	-2.399047	10	0.0374000	*
938.8159_11.193	rel_abund_log2	b1	b3	11	11	-2.473442	10	0.0329000	*
941.7313_7.957	rel_abund_log2	b1	b3	11	11	-3.103900	10	0.0112000	*
942.7986_8.961	rel_abund_log2	b1	b3	11	11	-2.270771	10	0.0465000	*
942.849_11.418	rel_abund_log2	b1	b3	11	11	-2.936790	10	0.0149000	*
944.7698_11.221	rel_abund_log2	b1	b3	11	11	-2.279081	10	0.0459000	*
950.8156_11.512	rel_abund_log2	b1	b3	11	11	-2.548337	10	0.0289000	*
951.7153_7.77	rel_abund_log2	b1	b3	11	11	-4.999709	10	0.0005380	***
963.8345_12.135	rel_abund_log2	b1	b3	11	11	2.847882	10	0.0173000	*
965.7308_7.761	rel_abund_log2	b1	b3	11	11	-3.565726	10	0.0051300	**
971.877_11.497	rel_abund_log2	b1	b3	11	11	-2.580789	10	0.0274000	*
972.8574_11.264	rel_abund_log2	b1	b3	11	11	3.466533	10	0.0060600	**
972.8_11.345	rel_abund_log2	b1	b3	11	11	-2.367070	10	0.0395000	*
993.7984_10.238	rel_abund_log2	b1	b3	11	11	-5.209554	10	0.0003960	***
994.7158_9.773	rel_abund_log2	b1	b3	11	11	2.229862	10	0.0499000	*

# how many are significant?
nrow(ctrl_t.test_paired_sig)

## [1] 438

ANOVA overlap

Keep sig features in t-test that have a match in sig ANOVA

sig_overlap_ctrl_paired <- inner_join(ctrl_t.test_paired_sig,
                                   trt_tukeyHSD_sig,
                                   by = "mz_rt",
                                   suffix = c(".t-test", ".tukeys"))

Paired beta

# run paired t-tests for control intervention
beta_t.test_paired <- df_for_stats %>%
  filter(treatment == "beta") %>%
 dplyr::select(subject, period, mz_rt, rel_abund_log2) %>%
  group_by(mz_rt) %>%
  t_test(rel_abund_log2 ~ period, 
         paired = TRUE, 
         p.adjust.method = "BH") %>% # Benjamini-Hochberg controlling to lower false positives
  add_significance()

Statistically significant features

# which features are significant?
beta_t.test_paired_sig <- beta_t.test_paired %>%
  filter(p < 0.05)
kable(beta_t.test_paired_sig)

mz_rt	.y.	group1	group2	n1	n2	statistic	df	p	p.signif
1002.658_3.079	rel_abund_log2	b1	b3	12	12	-2.907425	11	0.0143000	*
1014.9413_11.924	rel_abund_log2	b1	b3	12	12	2.850869	11	0.0158000	*
1017.6873_3.095	rel_abund_log2	b1	b3	12	12	2.490675	11	0.0300000	*
1028.9572_12.137	rel_abund_log2	b1	b3	12	12	3.196432	11	0.0085100	**
1034.9093_11.511	rel_abund_log2	b1	b3	12	12	-2.281384	11	0.0434000	*
1039.6688_3.106	rel_abund_log2	b1	b3	12	12	3.090821	11	0.0103000	*
1041.6842_3.059	rel_abund_log2	b1	b3	12	12	-2.372954	11	0.0370000	*
1041.9353_11.754	rel_abund_log2	b1	b3	12	12	-3.141742	11	0.0093800	**
1061.8543_10.506	rel_abund_log2	b1	b3	12	12	4.252810	11	0.0013600	**
1151.8656_7.71	rel_abund_log2	b1	b3	12	12	2.467303	11	0.0313000	*
116.0707_0.623	rel_abund_log2	b1	b3	12	12	3.044761	11	0.0112000	*
1163.3585_7.687	rel_abund_log2	b1	b3	12	12	3.087624	11	0.0103000	*
1169.3422_6.843	rel_abund_log2	b1	b3	12	12	-2.612715	11	0.0241000	*
118.0864_0.597	rel_abund_log2	b1	b3	12	12	-3.508925	11	0.0048900	**
1187.3609_7.137	rel_abund_log2	b1	b3	12	12	3.189925	11	0.0086100	**
1187.8642_7.137	rel_abund_log2	b1	b3	12	12	2.389894	11	0.0359000	*
1192.8412_6.709	rel_abund_log2	b1	b3	12	12	-2.791911	11	0.0175000	*
1193.9116_8.867	rel_abund_log2	b1	b3	12	12	2.545275	11	0.0272000	*
1198.856_7.141	rel_abund_log2	b1	b3	12	12	2.517719	11	0.0286000	*
1202.3827_8.089	rel_abund_log2	b1	b3	12	12	2.334710	11	0.0395000	*
1204.9038_8.864	rel_abund_log2	b1	b3	12	12	2.549782	11	0.0270000	*
1204.9948_9.897	rel_abund_log2	b1	b3	12	12	3.007036	11	0.0119000	*
1205.4054_8.863	rel_abund_log2	b1	b3	12	12	3.127086	11	0.0096300	**
1205.4975_9.898	rel_abund_log2	b1	b3	12	12	2.786117	11	0.0177000	*
1216.4896_9.892	rel_abund_log2	b1	b3	12	12	3.007214	11	0.0119000	*
1216.9911_9.896	rel_abund_log2	b1	b3	12	12	4.342452	11	0.0011700	**
1225.3783_7.964	rel_abund_log2	b1	b3	12	12	2.876849	11	0.0151000	*
1229.9108_8.397	rel_abund_log2	b1	b3	12	12	-3.226932	11	0.0080600	**
1229.9993_9.9	rel_abund_log2	b1	b3	12	12	-2.491862	11	0.0299000	*
1231.5117_9.905	rel_abund_log2	b1	b3	12	12	-3.287671	11	0.0072300	**
1241.4043_8.395	rel_abund_log2	b1	b3	12	12	-2.932053	11	0.0136000	*
1242.5051_10.22	rel_abund_log2	b1	b3	12	12	2.573750	11	0.0259000	*
1242.5053_9.906	rel_abund_log2	b1	b3	12	12	-3.280284	11	0.0073300	**
1250.8236_3.081	rel_abund_log2	b1	b3	12	12	-2.456499	11	0.0319000	*
1258.8122_3.08	rel_abund_log2	b1	b3	12	12	-2.365428	11	0.0375000	*
1259.7951_10.087	rel_abund_log2	b1	b3	12	12	2.532704	11	0.0278000	*
1263.8289_10.349	rel_abund_log2	b1	b3	12	12	6.183898	11	0.0000687	****
1266.8482_10.583	rel_abund_log2	b1	b3	12	12	3.365921	11	0.0063000	**
1268.1254_11.807	rel_abund_log2	b1	b3	12	12	-4.534893	11	0.0008510	***
1268.2053_12.079	rel_abund_log2	b1	b3	12	12	-2.466816	11	0.0313000	*
1281.8397_10.519	rel_abund_log2	b1	b3	12	12	2.821321	11	0.0166000	*
1289.1866_11.82	rel_abund_log2	b1	b3	12	12	-2.998637	11	0.0121000	*
1294.1419_11.81	rel_abund_log2	b1	b3	12	12	-2.602951	11	0.0246000	*
1297.1947_10.828	rel_abund_log2	b1	b3	12	12	3.787689	11	0.0030100	**
1298.1983_10.827	rel_abund_log2	b1	b3	12	12	6.342600	11	0.0000550	****
1302.2067_12.075	rel_abund_log2	b1	b3	12	12	2.352610	11	0.0383000	*
1308.157_11.948	rel_abund_log2	b1	b3	12	12	-3.043360	11	0.0112000	*
1312.1724_11.668	rel_abund_log2	b1	b3	12	12	-2.539744	11	0.0275000	*
1316.1245_11.664	rel_abund_log2	b1	b3	12	12	-2.750386	11	0.0189000	*
1319.2346_12.075	rel_abund_log2	b1	b3	12	12	2.566574	11	0.0262000	*
1322.2624_10.832	rel_abund_log2	b1	b3	12	12	3.264459	11	0.0075400	**
1334.1724_11.947	rel_abund_log2	b1	b3	12	12	-3.458028	11	0.0053500	**
1339.2017_11.786	rel_abund_log2	b1	b3	12	12	-2.820542	11	0.0167000	*
1341.0272_6.83	rel_abund_log2	b1	b3	12	12	3.014374	11	0.0118000	*
1341.2177_11.895	rel_abund_log2	b1	b3	12	12	-3.642532	11	0.0038700	**
1342.2211_11.896	rel_abund_log2	b1	b3	12	12	-3.644123	11	0.0038600	**
1346.1728_11.908	rel_abund_log2	b1	b3	12	12	-3.642554	11	0.0038700	**
1347.1765_11.909	rel_abund_log2	b1	b3	12	12	-3.893544	11	0.0025000	**
1364.2059_11.753	rel_abund_log2	b1	b3	12	12	-3.924465	11	0.0023700	**
1365.0287_6.836	rel_abund_log2	b1	b3	12	12	4.739490	11	0.0006100	***
1369.1611_11.766	rel_abund_log2	b1	b3	12	12	-3.238957	11	0.0078900	**
1389.0311_6.813	rel_abund_log2	b1	b3	12	12	2.572890	11	0.0259000	*
1447.1208_6.507	rel_abund_log2	b1	b3	12	12	-3.281292	11	0.0073200	**
1454.1867_9.904	rel_abund_log2	b1	b3	12	12	-2.668024	11	0.0219000	*
1454.1868_9.88	rel_abund_log2	b1	b3	12	12	-2.795578	11	0.0174000	*
1455.1905_9.914	rel_abund_log2	b1	b3	12	12	-2.656340	11	0.0223000	*
1470.0283_7.811	rel_abund_log2	b1	b3	12	12	-2.939774	11	0.0135000	*
1472.1218_6.472	rel_abund_log2	b1	b3	12	12	-2.692124	11	0.0209000	*
1473.1717_7.517	rel_abund_log2	b1	b3	12	12	-3.191936	11	0.0085800	**
1475.1518_7.004	rel_abund_log2	b1	b3	12	12	-4.310182	11	0.0012300	**
1476.1365_7.528	rel_abund_log2	b1	b3	12	12	-4.014291	11	0.0020400	**
1476.1552_7.005	rel_abund_log2	b1	b3	12	12	-4.317983	11	0.0012200	**
1478.1161_7.023	rel_abund_log2	b1	b3	12	12	-2.307626	11	0.0415000	*
1479.1185_7.024	rel_abund_log2	b1	b3	12	12	-5.825003	11	0.0001150	***
1479.137_7.538	rel_abund_log2	b1	b3	12	12	-2.332142	11	0.0397000	*
1481.1402_7.792	rel_abund_log2	b1	b3	12	12	-2.999802	11	0.0121000	*
1482.1063_6.531	rel_abund_log2	b1	b3	12	12	-2.366914	11	0.0374000	*
1484.1047_7.781	rel_abund_log2	b1	b3	12	12	-2.550518	11	0.0270000	*
1487.1511_6.872	rel_abund_log2	b1	b3	12	12	-2.229099	11	0.0476000	*
1488.1014_8.407	rel_abund_log2	b1	b3	12	12	2.657595	11	0.0223000	*
1488.1552_6.873	rel_abund_log2	b1	b3	12	12	-2.877212	11	0.0150000	*
1496.1206_6.463	rel_abund_log2	b1	b3	12	12	-2.382403	11	0.0363000	*
1497.1674_7.486	rel_abund_log2	b1	b3	12	12	-4.021558	11	0.0020100	**
1500.0993_6.518	rel_abund_log2	b1	b3	12	12	-3.062807	11	0.0108000	*
1500.1349_7.508	rel_abund_log2	b1	b3	12	12	-5.358192	11	0.0002310	***
1504.1822_7.513	rel_abund_log2	b1	b3	12	12	-2.275715	11	0.0439000	*
1506.1473_7.519	rel_abund_log2	b1	b3	12	12	-2.468064	11	0.0312000	*
1508.1474_7.516	rel_abund_log2	b1	b3	12	12	-3.918142	11	0.0024000	**
1509.1474_7.507	rel_abund_log2	b1	b3	12	12	-4.500309	11	0.0009010	***
1510.1192_7.922	rel_abund_log2	b1	b3	12	12	-2.754458	11	0.0187000	*
1510.1552_7.549	rel_abund_log2	b1	b3	12	12	-2.933067	11	0.0136000	*
1511.123_7.922	rel_abund_log2	b1	b3	12	12	-4.690336	11	0.0006600	***
1514.1517_7.514	rel_abund_log2	b1	b3	12	12	-3.459244	11	0.0053400	**
1518.2654_8.731	rel_abund_log2	b1	b3	12	12	2.673586	11	0.0217000	*
152.0706_0.642	rel_abund_log2	b1	b3	12	12	-2.316651	11	0.0408000	*
1520.1651_7.717	rel_abund_log2	b1	b3	12	12	3.093196	11	0.0102000	*
1524.1333_7.404	rel_abund_log2	b1	b3	12	12	-3.024948	11	0.0116000	*
1526.0908_8.976	rel_abund_log2	b1	b3	12	12	-3.585819	11	0.0042700	**
1526.1136_6.481	rel_abund_log2	b1	b3	12	12	-3.138942	11	0.0094300	**
1526.1519_7.532	rel_abund_log2	b1	b3	12	12	-2.610242	11	0.0242000	*
1527.094_8.977	rel_abund_log2	b1	b3	12	12	-4.278807	11	0.0013000	**
1531.181_7.771	rel_abund_log2	b1	b3	12	12	3.001248	11	0.0121000	*
1531.2124_8.09	rel_abund_log2	b1	b3	12	12	-4.380848	11	0.0011000	**
1532.1012_7.882	rel_abund_log2	b1	b3	12	12	-2.694609	11	0.0209000	*
1532.1582_7.713	rel_abund_log2	b1	b3	12	12	3.096079	11	0.0102000	*
1534.154_7.527	rel_abund_log2	b1	b3	12	12	-4.122997	11	0.0016900	**
1534.1768_8.083	rel_abund_log2	b1	b3	12	12	-2.484478	11	0.0303000	*
1535.1558_7.533	rel_abund_log2	b1	b3	12	12	-2.341287	11	0.0391000	*
1536.6252_6.856	rel_abund_log2	b1	b3	12	12	-2.949118	11	0.0132000	*
1537.1733_7.573	rel_abund_log2	b1	b3	12	12	-2.363906	11	0.0376000	*
1538.1147_6.455	rel_abund_log2	b1	b3	12	12	-2.299004	11	0.0421000	*
1538.1514_7.472	rel_abund_log2	b1	b3	12	12	-4.099077	11	0.0017600	**
1538.1799_7.62	rel_abund_log2	b1	b3	12	12	-2.515711	11	0.0287000	*
1540.1703_7.761	rel_abund_log2	b1	b3	12	12	-3.565141	11	0.0044300	**
1541.1773_7.535	rel_abund_log2	b1	b3	12	12	-3.263644	11	0.0075500	**
1542.1805_8.073	rel_abund_log2	b1	b3	12	12	-2.482580	11	0.0304000	*
1542.645_7.698	rel_abund_log2	b1	b3	12	12	2.320922	11	0.0405000	*
1543.2104_7.94	rel_abund_log2	b1	b3	12	12	-3.561288	11	0.0044600	**
1544.1542_7.691	rel_abund_log2	b1	b3	12	12	2.447667	11	0.0324000	*
1544.1549_7.686	rel_abund_log2	b1	b3	12	12	2.287555	11	0.0430000	*
1544.1615_7.508	rel_abund_log2	b1	b3	12	12	-2.691029	11	0.0210000	*
1546.1714_7.763	rel_abund_log2	b1	b3	12	12	2.713602	11	0.0202000	*
1546.1749_7.78	rel_abund_log2	b1	b3	12	12	3.126610	11	0.0096400	**
1547.1793_7.789	rel_abund_log2	b1	b3	12	12	2.479249	11	0.0306000	*
1547.1803_7.856	rel_abund_log2	b1	b3	12	12	2.804606	11	0.0171000	*
1547.1806_8.02	rel_abund_log2	b1	b3	12	12	2.279574	11	0.0436000	*
1548.0963_7.222	rel_abund_log2	b1	b3	12	12	-2.346615	11	0.0387000	*
1548.6599_7.695	rel_abund_log2	b1	b3	12	12	2.214510	11	0.0488000	*
1550.1138_6.449	rel_abund_log2	b1	b3	12	12	-5.303956	11	0.0002510	***
1550.1483_7.401	rel_abund_log2	b1	b3	12	12	-4.549974	11	0.0008300	***
1554.1833_7.701	rel_abund_log2	b1	b3	12	12	3.797293	11	0.0029600	**
1555.3432_11.744	rel_abund_log2	b1	b3	12	12	-2.618689	11	0.0239000	*
1556.1613_7.323	rel_abund_log2	b1	b3	12	12	-2.232542	11	0.0473000	*
1558.1201_7.783	rel_abund_log2	b1	b3	12	12	-2.746015	11	0.0190000	*
1558.1745_7.924	rel_abund_log2	b1	b3	12	12	-2.705676	11	0.0204000	*
1561.1392_7.955	rel_abund_log2	b1	b3	12	12	-2.561201	11	0.0265000	*
1562.1488_7.418	rel_abund_log2	b1	b3	12	12	-4.744829	11	0.0006050	***
1562.1493_7.418	rel_abund_log2	b1	b3	12	12	-4.667058	11	0.0006860	***
1563.1535_7.418	rel_abund_log2	b1	b3	12	12	-6.262429	11	0.0000615	****
1563.2185_8.966	rel_abund_log2	b1	b3	12	12	-3.841539	11	0.0027400	**
1564.1347_6.888	rel_abund_log2	b1	b3	12	12	-2.322371	11	0.0404000	*
1564.1662_7.508	rel_abund_log2	b1	b3	12	12	-3.267267	11	0.0075000	**
1566.1816_7.932	rel_abund_log2	b1	b3	12	12	-3.755427	11	0.0031800	**
1567.1909_7.931	rel_abund_log2	b1	b3	12	12	-3.886102	11	0.0025400	**
1568.6597_7.736	rel_abund_log2	b1	b3	12	12	-2.516232	11	0.0287000	*
1572.1304_7.394	rel_abund_log2	b1	b3	12	12	-3.879070	11	0.0025700	**
1574.1454_7.378	rel_abund_log2	b1	b3	12	12	-2.879446	11	0.0150000	*
1574.2638_8.923	rel_abund_log2	b1	b3	12	12	5.033478	11	0.0003820	***
1574.6139_6.862	rel_abund_log2	b1	b3	12	12	3.547225	11	0.0045700	**
1578.1434_7.057	rel_abund_log2	b1	b3	12	12	-2.315252	11	0.0409000	*
1578.178_7.924	rel_abund_log2	b1	b3	12	12	-4.339597	11	0.0011800	**
1579.649_7.139	rel_abund_log2	b1	b3	12	12	2.657987	11	0.0223000	*
1579.6508_7.286	rel_abund_log2	b1	b3	12	12	-2.458319	11	0.0318000	*
1580.1032_7.792	rel_abund_log2	b1	b3	12	12	-3.074112	11	0.0106000	*
1580.159_7.285	rel_abund_log2	b1	b3	12	12	-3.208273	11	0.0083300	**
1583.089_7.72	rel_abund_log2	b1	b3	12	12	2.469750	11	0.0311000	*
1583.178_7.489	rel_abund_log2	b1	b3	12	12	-2.505236	11	0.0292000	*
1584.1871_8.061	rel_abund_log2	b1	b3	12	12	2.629874	11	0.0234000	*
1585.2008_8.971	rel_abund_log2	b1	b3	12	12	-4.784666	11	0.0005670	***
1586.2043_8.972	rel_abund_log2	b1	b3	12	12	-4.676389	11	0.0006760	***
1589.2021_7.7	rel_abund_log2	b1	b3	12	12	2.258924	11	0.0452000	*
1590.143_7.137	rel_abund_log2	b1	b3	12	12	2.537882	11	0.0276000	*
1590.144_7.285	rel_abund_log2	b1	b3	12	12	-2.342018	11	0.0390000	*
1591.1476_7.288	rel_abund_log2	b1	b3	12	12	-2.285855	11	0.0431000	*
1591.1484_6.916	rel_abund_log2	b1	b3	12	12	-2.900142	11	0.0144000	*
1591.1826_7.956	rel_abund_log2	b1	b3	12	12	-4.174922	11	0.0015500	**
1591.2114_7.764	rel_abund_log2	b1	b3	12	12	-2.990003	11	0.0123000	*
1592.1591_7.656	rel_abund_log2	b1	b3	12	12	-2.222332	11	0.0482000	*
1592.5935_6.867	rel_abund_log2	b1	b3	12	12	-2.549150	11	0.0270000	*
1593.0936_6.87	rel_abund_log2	b1	b3	12	12	-2.317764	11	0.0407000	*
1593.594_6.87	rel_abund_log2	b1	b3	12	12	-2.429335	11	0.0334000	*
1594.0976_6.869	rel_abund_log2	b1	b3	12	12	-2.457502	11	0.0318000	*
1596.3093_9.895	rel_abund_log2	b1	b3	12	12	3.213292	11	0.0082600	**
1597.3131_9.894	rel_abund_log2	b1	b3	12	12	3.088396	11	0.0103000	*
1598.1451_7.197	rel_abund_log2	b1	b3	12	12	-2.980551	11	0.0125000	*
1604.1937_7.739	rel_abund_log2	b1	b3	12	12	-2.729686	11	0.0196000	*
1605.1979_7.674	rel_abund_log2	b1	b3	12	12	-3.537715	11	0.0046500	**
1609.235_8.392	rel_abund_log2	b1	b3	12	12	-3.127931	11	0.0096100	**
1610.32_9.892	rel_abund_log2	b1	b3	12	12	5.517444	11	0.0001810	***
1611.1156_6.852	rel_abund_log2	b1	b3	12	12	-2.209389	11	0.0493000	*
1611.8348_9.901	rel_abund_log2	b1	b3	12	12	2.452773	11	0.0321000	*
1616.16_7.754	rel_abund_log2	b1	b3	12	12	-2.720610	11	0.0199000	*
1618.2939_8.956	rel_abund_log2	b1	b3	12	12	2.881607	11	0.0149000	*
1619.2973_8.957	rel_abund_log2	b1	b3	12	12	2.703208	11	0.0205000	*
1621.1991_7.959	rel_abund_log2	b1	b3	12	12	-2.994891	11	0.0122000	*
1621.2757_7.96	rel_abund_log2	b1	b3	12	12	-2.697107	11	0.0208000	*
1623.2097_7.994	rel_abund_log2	b1	b3	12	12	-2.504542	11	0.0293000	*
1624.3414_10.412	rel_abund_log2	b1	b3	12	12	-2.318307	11	0.0407000	*
1625.2172_8.052	rel_abund_log2	b1	b3	12	12	-2.371787	11	0.0370000	*
1631.6823_7.958	rel_abund_log2	b1	b3	12	12	-2.777338	11	0.0180000	*
1636.3352_9.901	rel_abund_log2	b1	b3	12	12	-2.307434	11	0.0415000	*
1637.3363_9.913	rel_abund_log2	b1	b3	12	12	-2.593149	11	0.0250000	*
1642.1751_7.953	rel_abund_log2	b1	b3	12	12	-2.851929	11	0.0157000	*
1646.2057_7.997	rel_abund_log2	b1	b3	12	12	-2.579937	11	0.0256000	*
1648.3413_9.902	rel_abund_log2	b1	b3	12	12	-2.622360	11	0.0237000	*
1649.3444_10.225	rel_abund_log2	b1	b3	12	12	4.626105	11	0.0007330	***
1649.3448_9.904	rel_abund_log2	b1	b3	12	12	-3.976151	11	0.0021700	**
1677.4008_11.264	rel_abund_log2	b1	b3	12	12	-2.244614	11	0.0463000	*
1678.4082_11.291	rel_abund_log2	b1	b3	12	12	-2.572382	11	0.0259000	*
188.0707_0.633	rel_abund_log2	b1	b3	12	12	2.908822	11	0.0142000	*
205.1221_0.621	rel_abund_log2	b1	b3	12	12	3.428639	11	0.0056400	**
235.0925_0.646	rel_abund_log2	b1	b3	12	12	-4.941466	11	0.0004420	***
247.1441_0.636	rel_abund_log2	b1	b3	12	12	2.727764	11	0.0197000	*
286.1437_1.13	rel_abund_log2	b1	b3	12	12	-5.875625	11	0.0001070	***
286.2013_0.633	rel_abund_log2	b1	b3	12	12	-2.377728	11	0.0366000	*
308.1855_0.633	rel_abund_log2	b1	b3	12	12	-11.533175	11	0.0000002	****
312.1597_1.413	rel_abund_log2	b1	b3	12	12	-7.704638	11	0.0000093	****
316.2483_0.679	rel_abund_log2	b1	b3	12	12	3.066815	11	0.0107000	*
316.2608_0.679	rel_abund_log2	b1	b3	12	12	4.574642	11	0.0007970	***
330.2637_0.689	rel_abund_log2	b1	b3	12	12	2.692511	11	0.0209000	*
331.0535_0.634	rel_abund_log2	b1	b3	12	12	2.255705	11	0.0454000	*
332.243_0.64	rel_abund_log2	b1	b3	12	12	3.333947	11	0.0066600	**
337.1048_1.268	rel_abund_log2	b1	b3	12	12	-2.540238	11	0.0275000	*
342.2638_0.69	rel_abund_log2	b1	b3	12	12	2.555970	11	0.0267000	*
344.2792_0.693	rel_abund_log2	b1	b3	12	12	2.622212	11	0.0237000	*
344.2793_0.927	rel_abund_log2	b1	b3	12	12	3.974884	11	0.0021800	**
370.3549_12.075	rel_abund_log2	b1	b3	12	12	-2.218826	11	0.0485000	*
373.2483_1.147	rel_abund_log2	b1	b3	12	12	-5.718780	11	0.0001340	***
428.3732_2.759	rel_abund_log2	b1	b3	12	12	2.850698	11	0.0158000	*
438.2976_3.525	rel_abund_log2	b1	b3	12	12	-2.437815	11	0.0330000	*
464.1914_0.656	rel_abund_log2	b1	b3	12	12	-4.948715	11	0.0004370	***
466.3289_4.123	rel_abund_log2	b1	b3	12	12	-4.152932	11	0.0016100	**
480.3082_3.357	rel_abund_log2	b1	b3	12	12	4.801752	11	0.0005520	***
480.3446_3.381	rel_abund_log2	b1	b3	12	12	-2.568285	11	0.0261000	*
482.3238_2.701	rel_abund_log2	b1	b3	12	12	-3.979185	11	0.0021600	**
502.2902_3.355	rel_abund_log2	b1	b3	12	12	3.889509	11	0.0025200	**
510.3551_3.445	rel_abund_log2	b1	b3	12	12	-3.509764	11	0.0048900	**
518.322_3.062	rel_abund_log2	b1	b3	12	12	2.460280	11	0.0317000	*
518.4564_5.944	rel_abund_log2	b1	b3	12	12	-2.496145	11	0.0297000	*
522.3564_3.24	rel_abund_log2	b1	b3	12	12	3.468783	11	0.0052500	**
526.2644_1.714	rel_abund_log2	b1	b3	12	12	-9.706981	11	0.0000010	****
534.2952_3.067	rel_abund_log2	b1	b3	12	12	2.237649	11	0.0469000	*
536.437_10.971	rel_abund_log2	b1	b3	12	12	7.534995	11	0.0000115	****
536.437_11.201	rel_abund_log2	b1	b3	12	12	-10.744586	11	0.0000004	****
538.3859_4.051	rel_abund_log2	b1	b3	12	12	-4.109516	11	0.0017300	**
544.3374_3.238	rel_abund_log2	b1	b3	12	12	3.214357	11	0.0082400	**
550.3862_3.859	rel_abund_log2	b1	b3	12	12	2.347689	11	0.0386000	*
552.4018_4.301	rel_abund_log2	b1	b3	12	12	2.639037	11	0.0230000	*
563.4268_4.262	rel_abund_log2	b1	b3	12	12	2.695471	11	0.0208000	*
563.4273_4.415	rel_abund_log2	b1	b3	12	12	3.662361	11	0.0037400	**
577.5185_8.358	rel_abund_log2	b1	b3	12	12	4.649623	11	0.0007060	***
593.4757_4.54	rel_abund_log2	b1	b3	12	12	5.662733	11	0.0001460	***
593.4762_4.644	rel_abund_log2	b1	b3	12	12	5.087222	11	0.0003510	***
593.4768_4.312	rel_abund_log2	b1	b3	12	12	2.871921	11	0.0152000	*
595.4929_4.645	rel_abund_log2	b1	b3	12	12	5.671726	11	0.0001440	***
596.4154_4.906	rel_abund_log2	b1	b3	12	12	-9.941480	11	0.0000008	****
597.5086_5.242	rel_abund_log2	b1	b3	12	12	2.764102	11	0.0184000	*
605.5497_9.629	rel_abund_log2	b1	b3	12	12	5.676363	11	0.0001430	***
608.4637_5.649	rel_abund_log2	b1	b3	12	12	2.599679	11	0.0247000	*
610.431_5.16	rel_abund_log2	b1	b3	12	12	-5.115381	11	0.0003360	***
617.4748_4.643	rel_abund_log2	b1	b3	12	12	3.692115	11	0.0035500	**
619.4903_4.953	rel_abund_log2	b1	b3	12	12	10.205135	11	0.0000006	****
620.5974_10.522	rel_abund_log2	b1	b3	12	12	3.526577	11	0.0047400	**
622.6132_10.675	rel_abund_log2	b1	b3	12	12	3.309903	11	0.0069500	**
629.1522_0.633	rel_abund_log2	b1	b3	12	12	2.743936	11	0.0191000	*
634.5399_9.061	rel_abund_log2	b1	b3	12	12	-2.845417	11	0.0159000	*
634.54_8.941	rel_abund_log2	b1	b3	12	12	-4.213168	11	0.0014500	**
642.6107_12.077	rel_abund_log2	b1	b3	12	12	-2.949128	11	0.0132000	*
643.38_0.69	rel_abund_log2	b1	b3	12	12	-3.215580	11	0.0082200	**
650.6449_10.83	rel_abund_log2	b1	b3	12	12	4.716463	11	0.0006330	***
652.6596_10.839	rel_abund_log2	b1	b3	12	12	4.249561	11	0.0013700	**
661.5282_5.288	rel_abund_log2	b1	b3	12	12	-3.580737	11	0.0043100	**
664.6011_11.668	rel_abund_log2	b1	b3	12	12	-3.993949	11	0.0021100	**
672.3751_3.941	rel_abund_log2	b1	b3	12	12	2.692089	11	0.0210000	*
673.5278_5.092	rel_abund_log2	b1	b3	12	12	-2.395664	11	0.0355000	*
673.5905_11.756	rel_abund_log2	b1	b3	12	12	-3.864084	11	0.0026300	**
677.5582_5.973	rel_abund_log2	b1	b3	12	12	-3.691172	11	0.0035600	**
677.5586_5.973	rel_abund_log2	b1	b3	12	12	-3.468487	11	0.0052500	**
690.6188_11.754	rel_abund_log2	b1	b3	12	12	-4.145889	11	0.0016300	**
691.5742_6.403	rel_abund_log2	b1	b3	12	12	-3.237647	11	0.0079100	**
692.5581_7.191	rel_abund_log2	b1	b3	12	12	-2.368246	11	0.0373000	*
692.6335_11.899	rel_abund_log2	b1	b3	12	12	-4.359966	11	0.0011400	**
693.6368_11.899	rel_abund_log2	b1	b3	12	12	-4.471562	11	0.0009440	***
695.5093_5.094	rel_abund_log2	b1	b3	12	12	-2.729085	11	0.0196000	*
695.5729_11.772	rel_abund_log2	b1	b3	12	12	-3.680719	11	0.0036200	**
700.5274_7.902	rel_abund_log2	b1	b3	12	12	-3.566365	11	0.0044200	**
717.5903_6.828	rel_abund_log2	b1	b3	12	12	-3.320846	11	0.0068200	**
717.5903_7.018	rel_abund_log2	b1	b3	12	12	-4.789410	11	0.0005630	***
718.5742_8.138	rel_abund_log2	b1	b3	12	12	-2.892058	11	0.0147000	*
718.5934_7.015	rel_abund_log2	b1	b3	12	12	-4.085874	11	0.0018000	**
719.6006_7.392	rel_abund_log2	b1	b3	12	12	-3.116536	11	0.0098100	**
720.5532_7.039	rel_abund_log2	b1	b3	12	12	-3.420449	11	0.0057200	**
721.5835_6.066	rel_abund_log2	b1	b3	12	12	-2.359152	11	0.0379000	*
721.5846_6.067	rel_abund_log2	b1	b3	12	12	-3.592012	11	0.0042300	**
724.5277_7.806	rel_abund_log2	b1	b3	12	12	-3.031028	11	0.0114000	*
726.5428_8.237	rel_abund_log2	b1	b3	12	12	-6.335314	11	0.0000556	****
727.5745_6.067	rel_abund_log2	b1	b3	12	12	-2.898839	11	0.0145000	*
728.5588_9.081	rel_abund_log2	b1	b3	12	12	-2.677997	11	0.0215000	*
732.589_8.53	rel_abund_log2	b1	b3	12	12	-5.553212	11	0.0001720	***
733.6212_7.929	rel_abund_log2	b1	b3	12	12	-3.021619	11	0.0116000	*
733.6215_7.929	rel_abund_log2	b1	b3	12	12	-3.426096	11	0.0056600	**
738.543_8.181	rel_abund_log2	b1	b3	12	12	-4.159909	11	0.0015900	**
740.5626_6.841	rel_abund_log2	b1	b3	12	12	-2.392254	11	0.0357000	*
740.5662_6.833	rel_abund_log2	b1	b3	12	12	-3.412049	11	0.0058000	**
742.5377_7.462	rel_abund_log2	b1	b3	12	12	2.323889	11	0.0403000	*
742.5718_8.28	rel_abund_log2	b1	b3	12	12	-4.627766	11	0.0007310	***
742.5753_7.523	rel_abund_log2	b1	b3	12	12	-3.791593	11	0.0029900	**
743.6054_7.227	rel_abund_log2	b1	b3	12	12	-4.501344	11	0.0008990	***
744.5538_6.51	rel_abund_log2	b1	b3	12	12	-2.834486	11	0.0162000	*
745.6202_7.852	rel_abund_log2	b1	b3	12	12	-4.131288	11	0.0016700	**
745.6214_8.115	rel_abund_log2	b1	b3	12	12	-3.618958	11	0.0040300	**
746.5091_7.814	rel_abund_log2	b1	b3	12	12	-2.915875	11	0.0140000	*
746.5685_9.239	rel_abund_log2	b1	b3	12	12	4.731171	11	0.0006180	***
746.6055_8.435	rel_abund_log2	b1	b3	12	12	2.295541	11	0.0424000	*
748.5258_8.233	rel_abund_log2	b1	b3	12	12	-4.230601	11	0.0014100	**
748.5844_7.89	rel_abund_log2	b1	b3	12	12	-2.820936	11	0.0166000	*
748.5844_8.099	rel_abund_log2	b1	b3	12	12	-2.443001	11	0.0326000	*
749.5557_6.066	rel_abund_log2	b1	b3	12	12	-2.862613	11	0.0154000	*
750.7353_10.695	rel_abund_log2	b1	b3	12	12	-2.258322	11	0.0452000	*
752.5592_8.969	rel_abund_log2	b1	b3	12	12	-2.583419	11	0.0254000	*
752.5594_8.973	rel_abund_log2	b1	b3	12	12	-4.162236	11	0.0015800	**
754.5373_6.002	rel_abund_log2	b1	b3	12	12	-2.531715	11	0.0279000	*
755.4833_5.662	rel_abund_log2	b1	b3	12	12	-3.759038	11	0.0031600	**
755.6054_6.838	rel_abund_log2	b1	b3	12	12	-3.565031	11	0.0044300	**
756.5904_7.889	rel_abund_log2	b1	b3	12	12	-4.104934	11	0.0017400	**
758.9871_6.983	rel_abund_log2	b1	b3	12	12	3.725629	11	0.0033500	**
759.0903_6.983	rel_abund_log2	b1	b3	12	12	2.428122	11	0.0335000	*
759.1221_6.983	rel_abund_log2	b1	b3	12	12	2.281666	11	0.0434000	*
759.6382_8.714	rel_abund_log2	b1	b3	12	12	2.723708	11	0.0198000	*
760.4052_6.983	rel_abund_log2	b1	b3	12	12	2.945009	11	0.0133000	*
760.5883_7.719	rel_abund_log2	b1	b3	12	12	3.494677	11	0.0050200	**
761.1927_7.717	rel_abund_log2	b1	b3	12	12	2.488159	11	0.0301000	*
761.2595_7.716	rel_abund_log2	b1	b3	12	12	2.589685	11	0.0252000	*
761.302_7.716	rel_abund_log2	b1	b3	12	12	2.769744	11	0.0182000	*
761.3174_7.717	rel_abund_log2	b1	b3	12	12	3.759162	11	0.0031600	**
761.329_7.717	rel_abund_log2	b1	b3	12	12	2.777150	11	0.0180000	*
761.3681_7.718	rel_abund_log2	b1	b3	12	12	3.246429	11	0.0077800	**
761.3781_7.717	rel_abund_log2	b1	b3	12	12	2.463379	11	0.0315000	*
761.4062_7.717	rel_abund_log2	b1	b3	12	12	2.577822	11	0.0257000	*
761.4545_7.719	rel_abund_log2	b1	b3	12	12	2.883713	11	0.0149000	*
761.5191_7.716	rel_abund_log2	b1	b3	12	12	2.840790	11	0.0161000	*
761.6526_9.142	rel_abund_log2	b1	b3	12	12	-2.341413	11	0.0391000	*
762.5032_7.719	rel_abund_log2	b1	b3	12	12	3.058724	11	0.0109000	*
762.5251_7.716	rel_abund_log2	b1	b3	12	12	2.502095	11	0.0294000	*
762.5433_7.717	rel_abund_log2	b1	b3	12	12	3.159319	11	0.0090900	**
764.5195_7.458	rel_abund_log2	b1	b3	12	12	3.488784	11	0.0050700	**
766.5755_7.417	rel_abund_log2	b1	b3	12	12	-4.948182	11	0.0004370	***
768.5536_6.434	rel_abund_log2	b1	b3	12	12	-4.877241	11	0.0004890	***
768.587_8.451	rel_abund_log2	b1	b3	12	12	3.669638	11	0.0036900	**
770.0615_7.009	rel_abund_log2	b1	b3	12	12	2.393102	11	0.0357000	*
770.5691_6.825	rel_abund_log2	b1	b3	12	12	-4.461206	11	0.0009610	***
770.6056_8.664	rel_abund_log2	b1	b3	12	12	-3.449796	11	0.0054300	**
771.5762_7.712	rel_abund_log2	b1	b3	12	12	2.550190	11	0.0270000	*
771.6366_8.357	rel_abund_log2	b1	b3	12	12	-2.850766	11	0.0158000	*
772.5855_7.501	rel_abund_log2	b1	b3	12	12	-2.816455	11	0.0168000	*
772.6196_8.643	rel_abund_log2	b1	b3	12	12	5.454018	11	0.0002000	***
773.6516_9.025	rel_abund_log2	b1	b3	12	12	-5.477147	11	0.0001930	***
774.5401_8.626	rel_abund_log2	b1	b3	12	12	-2.651177	11	0.0225000	*
774.5404_8.982	rel_abund_log2	b1	b3	12	12	-3.436479	11	0.0055600	**
775.6384_6.825	rel_abund_log2	b1	b3	12	12	3.134161	11	0.0095100	**
775.6661_9.814	rel_abund_log2	b1	b3	12	12	-5.633065	11	0.0001530	***
778.5376_5.842	rel_abund_log2	b1	b3	12	12	-2.247271	11	0.0461000	*
780.5519_6.84	rel_abund_log2	b1	b3	12	12	2.252107	11	0.0457000	*
780.5795_10.219	rel_abund_log2	b1	b3	12	12	-2.855147	11	0.0157000	*
780.5921_7.79	rel_abund_log2	b1	b3	12	12	-4.826367	11	0.0005300	***
781.6192_8.718	rel_abund_log2	b1	b3	12	12	2.578271	11	0.0257000	*
781.62_7.043	rel_abund_log2	b1	b3	12	12	-3.769563	11	0.0031000	**
783.3522_6.877	rel_abund_log2	b1	b3	12	12	-2.555880	11	0.0267000	*
783.4174_6.877	rel_abund_log2	b1	b3	12	12	-2.422076	11	0.0339000	*
783.5024_6.875	rel_abund_log2	b1	b3	12	12	-2.262701	11	0.0449000	*
783.6349_9.156	rel_abund_log2	b1	b3	12	12	-2.240186	11	0.0467000	*
783.6368_7.847	rel_abund_log2	b1	b3	12	12	-4.044902	11	0.0019300	**
784.5879_7.141	rel_abund_log2	b1	b3	12	12	2.769828	11	0.0182000	*
784.5881_7.29	rel_abund_log2	b1	b3	12	12	-2.281226	11	0.0434000	*
784.6654_10.38	rel_abund_log2	b1	b3	12	12	6.297234	11	0.0000586	****
785.452_7.291	rel_abund_log2	b1	b3	12	12	-2.370014	11	0.0372000	*
785.5792_6.874	rel_abund_log2	b1	b3	12	12	-2.419479	11	0.0340000	*
785.6536_8.747	rel_abund_log2	b1	b3	12	12	-3.288490	11	0.0072200	**
785.6542_8.953	rel_abund_log2	b1	b3	12	12	4.521950	11	0.0008690	***
786.6508_7.289	rel_abund_log2	b1	b3	12	12	-2.405576	11	0.0349000	*
787.6674_9.202	rel_abund_log2	b1	b3	12	12	-5.894262	11	0.0001040	***
787.6701_9.896	rel_abund_log2	b1	b3	12	12	3.541030	11	0.0046200	**
788.5563_7.415	rel_abund_log2	b1	b3	12	12	-3.337840	11	0.0066200	**
788.6177_8.867	rel_abund_log2	b1	b3	12	12	2.592324	11	0.0250000	*
789.0076_8.052	rel_abund_log2	b1	b3	12	12	-2.984137	11	0.0124000	*
790.5352_6.424	rel_abund_log2	b1	b3	12	12	-2.450170	11	0.0322000	*
790.5744_7.193	rel_abund_log2	b1	b3	12	12	-2.919270	11	0.0140000	*
792.5512_6.827	rel_abund_log2	b1	b3	12	12	-3.131721	11	0.0095500	**
792.5531_6.261	rel_abund_log2	b1	b3	12	12	-2.808658	11	0.0170000	*
792.5873_8.665	rel_abund_log2	b1	b3	12	12	-2.479650	11	0.0306000	*
792.5893_7.058	rel_abund_log2	b1	b3	12	12	2.285182	11	0.0431000	*
793.556_6.829	rel_abund_log2	b1	b3	12	12	-2.329566	11	0.0399000	*
794.0604_7.102	rel_abund_log2	b1	b3	12	12	2.287932	11	0.0429000	*
794.5862_7.544	rel_abund_log2	b1	b3	12	12	-3.071435	11	0.0106000	*
794.6054_8.214	rel_abund_log2	b1	b3	12	12	-2.235669	11	0.0471000	*
794.6057_8.544	rel_abund_log2	b1	b3	12	12	-2.364109	11	0.0375000	*
796.5222_9.007	rel_abund_log2	b1	b3	12	12	-3.128602	11	0.0096000	**
796.5247_6.843	rel_abund_log2	b1	b3	12	12	2.341802	11	0.0390000	*
796.5845_7.27	rel_abund_log2	b1	b3	12	12	-3.313980	11	0.0069000	**
796.5848_7.423	rel_abund_log2	b1	b3	12	12	-4.714368	11	0.0006350	***
797.6383_8.603	rel_abund_log2	b1	b3	12	12	-2.253934	11	0.0456000	*
797.6421_8.39	rel_abund_log2	b1	b3	12	12	-4.407024	11	0.0010500	**
797.6453_8.401	rel_abund_log2	b1	b3	12	12	-3.383163	11	0.0061100	**
798.6003_7.83	rel_abund_log2	b1	b3	12	12	-4.985374	11	0.0004120	***
798.635_8.894	rel_abund_log2	b1	b3	12	12	2.305605	11	0.0416000	*
798.6518_8.391	rel_abund_log2	b1	b3	12	12	-4.437323	11	0.0009990	***
798.681_10.534	rel_abund_log2	b1	b3	12	12	-2.915145	11	0.0141000	*
799.6069_8.865	rel_abund_log2	b1	b3	12	12	2.780380	11	0.0179000	*
799.6686_9.32	rel_abund_log2	b1	b3	12	12	-4.367844	11	0.0011200	**
800.6163_8.637	rel_abund_log2	b1	b3	12	12	-2.975289	11	0.0126000	*
800.616_8.426	rel_abund_log2	b1	b3	12	12	-2.496872	11	0.0297000	*
801.686_10.409	rel_abund_log2	b1	b3	12	12	-2.318134	11	0.0407000	*
802.5715_9.823	rel_abund_log2	b1	b3	12	12	-2.409173	11	0.0347000	*
802.5716_10.204	rel_abund_log2	b1	b3	12	12	-2.503675	11	0.0293000	*
802.5717_7.81	rel_abund_log2	b1	b3	12	12	-2.983925	11	0.0124000	*
804.5937_7.544	rel_abund_log2	b1	b3	12	12	-2.852410	11	0.0157000	*
805.0108_3.238	rel_abund_log2	b1	b3	12	12	2.694286	11	0.0209000	*
805.6187_7.848	rel_abund_log2	b1	b3	12	12	-3.491854	11	0.0050400	**
806.5698_6.37	rel_abund_log2	b1	b3	12	12	-4.798193	11	0.0005550	***
806.6235_6.835	rel_abund_log2	b1	b3	12	12	-2.375917	11	0.0368000	*
807.6349_8.948	rel_abund_log2	b1	b3	12	12	5.533365	11	0.0001770	***
807.6359_7.483	rel_abund_log2	b1	b3	12	12	-2.802417	11	0.0172000	*
808.6375_8.751	rel_abund_log2	b1	b3	12	12	-5.062717	11	0.0003650	***
809.6507_9.876	rel_abund_log2	b1	b3	12	12	4.451535	11	0.0009760	***
809.6525_8.065	rel_abund_log2	b1	b3	12	12	-4.513772	11	0.0008810	***
810.5875_6.727	rel_abund_log2	b1	b3	12	12	-2.646499	11	0.0227000	*
810.5988_8.865	rel_abund_log2	b1	b3	12	12	2.859012	11	0.0155000	*
810.6013_7.646	rel_abund_log2	b1	b3	12	12	-3.244974	11	0.0078000	**
810.6041_7.959	rel_abund_log2	b1	b3	12	12	-2.578837	11	0.0256000	*
810.6564_8.03	rel_abund_log2	b1	b3	12	12	-4.410515	11	0.0010400	**
810.6812_10.387	rel_abund_log2	b1	b3	12	12	-2.221920	11	0.0482000	*
811.0052_7.959	rel_abund_log2	b1	b3	12	12	-2.375343	11	0.0368000	*
811.2977_7.959	rel_abund_log2	b1	b3	12	12	-3.939843	11	0.0023100	**
811.3445_7.959	rel_abund_log2	b1	b3	12	12	-2.330306	11	0.0398000	*
811.6292_7.819	rel_abund_log2	b1	b3	12	12	-4.459812	11	0.0009630	***
811.6598_8.009	rel_abund_log2	b1	b3	12	12	-2.694497	11	0.0209000	*
811.6703_8.961	rel_abund_log2	b1	b3	12	12	-2.739847	11	0.0192000	*
812.545_7.958	rel_abund_log2	b1	b3	12	12	-2.658052	11	0.0223000	*
812.5561_7.198	rel_abund_log2	b1	b3	12	12	-2.416942	11	0.0342000	*
812.615_8.136	rel_abund_log2	b1	b3	12	12	2.723360	11	0.0198000	*
812.6186_8.396	rel_abund_log2	b1	b3	12	12	-2.321182	11	0.0405000	*
812.6971_10.626	rel_abund_log2	b1	b3	12	12	2.745936	11	0.0190000	*
813.6106_7.959	rel_abund_log2	b1	b3	12	12	-2.439900	11	0.0328000	*
813.6834_9.437	rel_abund_log2	b1	b3	12	12	-8.260860	11	0.0000048	****
813.6854_10.226	rel_abund_log2	b1	b3	12	12	4.605534	11	0.0007580	***
813.6866_9.902	rel_abund_log2	b1	b3	12	12	-3.307537	11	0.0069800	**
814.6318_9.087	rel_abund_log2	b1	b3	12	12	-2.237745	11	0.0469000	*
815.5406_6.313	rel_abund_log2	b1	b3	12	12	-2.920939	11	0.0139000	*
815.6606_9.084	rel_abund_log2	b1	b3	12	12	-2.539585	11	0.0275000	*
815.7025_10.568	rel_abund_log2	b1	b3	12	12	4.078741	11	0.0018200	**
816.5873_8.216	rel_abund_log2	b1	b3	12	12	-3.281420	11	0.0073200	**
816.7069_11.125	rel_abund_log2	b1	b3	12	12	-2.221497	11	0.0482000	*
817.7051_10.344	rel_abund_log2	b1	b3	12	12	-5.759460	11	0.0001270	***
818.5668_7.42	rel_abund_log2	b1	b3	12	12	-3.105735	11	0.0100000	**
818.6044_8.299	rel_abund_log2	b1	b3	12	12	-2.408189	11	0.0347000	*
819.5737_7.769	rel_abund_log2	b1	b3	12	12	-2.234054	11	0.0472000	*
820.5846_7.213	rel_abund_log2	b1	b3	12	12	-3.579088	11	0.0043300	**
820.6961_11.574	rel_abund_log2	b1	b3	12	12	-3.220326	11	0.0081500	**
821.5898_7.956	rel_abund_log2	b1	b3	12	12	-2.442463	11	0.0327000	*
822.5404_7.148	rel_abund_log2	b1	b3	12	12	2.833266	11	0.0163000	*
822.5994_7.509	rel_abund_log2	b1	b3	12	12	-3.516004	11	0.0048300	**
824.616_8.544	rel_abund_log2	b1	b3	12	12	-3.732345	11	0.0033100	**
824.6526_9.963	rel_abund_log2	b1	b3	12	12	2.305562	11	0.0416000	*
825.6241_9.886	rel_abund_log2	b1	b3	12	12	2.813073	11	0.0169000	*
825.6473_7.81	rel_abund_log2	b1	b3	12	12	-2.795024	11	0.0174000	*
825.6815_9.335	rel_abund_log2	b1	b3	12	12	-8.248568	11	0.0000049	****
825.682_9.561	rel_abund_log2	b1	b3	12	12	-3.896459	11	0.0024900	**
827.663_8.483	rel_abund_log2	b1	b3	12	12	-4.566489	11	0.0008080	***
827.6998_10.277	rel_abund_log2	b1	b3	12	12	-6.180185	11	0.0000691	****
827.6999_10.389	rel_abund_log2	b1	b3	12	12	-3.387168	11	0.0060700	**
827.6999_10.502	rel_abund_log2	b1	b3	12	12	-4.084454	11	0.0018100	**
828.5511_6.373	rel_abund_log2	b1	b3	12	12	-4.255520	11	0.0013500	**
828.702_10.497	rel_abund_log2	b1	b3	12	12	-4.291898	11	0.0012700	**
829.6782_8.401	rel_abund_log2	b1	b3	12	12	-3.647517	11	0.0038400	**
829.6788_9.393	rel_abund_log2	b1	b3	12	12	-2.244055	11	0.0464000	*
829.7155_10.657	rel_abund_log2	b1	b3	12	12	-4.498199	11	0.0009040	***
830.568_6.228	rel_abund_log2	b1	b3	12	12	-2.650116	11	0.0226000	*
831.6353_8.196	rel_abund_log2	b1	b3	12	12	-5.567245	11	0.0001680	***
831.6573_8.408	rel_abund_log2	b1	b3	12	12	3.355279	11	0.0064200	**
831.694_9.318	rel_abund_log2	b1	b3	12	12	-2.990044	11	0.0123000	*
832.5817_7.451	rel_abund_log2	b1	b3	12	12	3.069581	11	0.0107000	*
832.582_7.631	rel_abund_log2	b1	b3	12	12	-5.256063	11	0.0002700	***
832.7384_11.319	rel_abund_log2	b1	b3	12	12	-2.299725	11	0.0421000	*
833.6507_8.971	rel_abund_log2	b1	b3	12	12	-2.637073	11	0.0231000	*
834.5987_8.394	rel_abund_log2	b1	b3	12	12	-3.247121	11	0.0077700	**
834.6002_7.257	rel_abund_log2	b1	b3	12	12	-4.533074	11	0.0008540	***
835.6566_8.968	rel_abund_log2	b1	b3	12	12	-2.562955	11	0.0264000	*
835.6663_8.407	rel_abund_log2	b1	b3	12	12	-2.903886	11	0.0143000	*
835.6664_10.22	rel_abund_log2	b1	b3	12	12	5.437028	11	0.0002050	***
835.6666_9.91	rel_abund_log2	b1	b3	12	12	-3.738576	11	0.0032700	**
836.6136_9.086	rel_abund_log2	b1	b3	12	12	-2.461375	11	0.0316000	*
836.6141_8.438	rel_abund_log2	b1	b3	12	12	-2.271088	11	0.0442000	*
837.5477_8.363	rel_abund_log2	b1	b3	12	12	6.077289	11	0.0000799	****
837.614_7.764	rel_abund_log2	b1	b3	12	12	-2.776988	11	0.0180000	*
837.6789_10.324	rel_abund_log2	b1	b3	12	12	-3.164589	11	0.0090100	**
837.6818_10.567	rel_abund_log2	b1	b3	12	12	2.477574	11	0.0307000	*
837.6831_9.217	rel_abund_log2	b1	b3	12	12	-4.910826	11	0.0004640	***
838.6301_9.147	rel_abund_log2	b1	b3	12	12	3.159909	11	0.0090800	**
838.6321_8.779	rel_abund_log2	b1	b3	12	12	-2.703999	11	0.0205000	*
839.6989_10.178	rel_abund_log2	b1	b3	12	12	-3.027445	11	0.0115000	*
842.5663_7.213	rel_abund_log2	b1	b3	12	12	-3.157533	11	0.0091200	**
842.723_11.155	rel_abund_log2	b1	b3	12	12	-3.571412	11	0.0043800	**
844.7397_11.265	rel_abund_log2	b1	b3	12	12	-3.024480	11	0.0116000	*
847.6893_7.716	rel_abund_log2	b1	b3	12	12	2.688455	11	0.0211000	*
848.6521_9.028	rel_abund_log2	b1	b3	12	12	-6.444575	11	0.0000478	****
849.6939_11.262	rel_abund_log2	b1	b3	12	12	-2.713895	11	0.0202000	*
850.5715_8.394	rel_abund_log2	b1	b3	12	12	-2.530458	11	0.0280000	*
851.64_9.902	rel_abund_log2	b1	b3	12	12	-3.264642	11	0.0075400	**
852.6843_10.599	rel_abund_log2	b1	b3	12	12	2.865604	11	0.0154000	*
853.6758_9.321	rel_abund_log2	b1	b3	12	12	-3.186738	11	0.0086600	**
853.6786_8.974	rel_abund_log2	b1	b3	12	12	-3.872336	11	0.0026000	**
853.6786_8.975	rel_abund_log2	b1	b3	12	12	-2.733762	11	0.0194000	*
854.574_8.362	rel_abund_log2	b1	b3	12	12	2.744565	11	0.0191000	*
856.5819_7.24	rel_abund_log2	b1	b3	12	12	-2.635507	11	0.0232000	*
856.5823_7.778	rel_abund_log2	b1	b3	12	12	-2.662913	11	0.0221000	*
858.5914_7.792	rel_abund_log2	b1	b3	12	12	-2.971030	11	0.0127000	*
858.5977_8.453	rel_abund_log2	b1	b3	12	12	-2.535463	11	0.0277000	*
858.5992_7.06	rel_abund_log2	b1	b3	12	12	-2.434204	11	0.0332000	*
858.7545_11.345	rel_abund_log2	b1	b3	12	12	-4.026376	11	0.0019900	**
859.53_8.35	rel_abund_log2	b1	b3	12	12	8.076764	11	0.0000060	****
859.6891_7.519	rel_abund_log2	b1	b3	12	12	-2.565367	11	0.0263000	*
860.6131_9.152	rel_abund_log2	b1	b3	12	12	2.574059	11	0.0259000	*
860.6133_8.785	rel_abund_log2	b1	b3	12	12	-3.529482	11	0.0047200	**
861.7048_7.713	rel_abund_log2	b1	b3	12	12	2.431063	11	0.0333000	*
863.7199_8.448	rel_abund_log2	b1	b3	12	12	2.266135	11	0.0446000	*
865.5795_9.626	rel_abund_log2	b1	b3	12	12	7.369271	11	0.0000141	****
868.7389_11.19	rel_abund_log2	b1	b3	12	12	-3.608625	11	0.0041100	**
870.6336_9.039	rel_abund_log2	b1	b3	12	12	-2.821599	11	0.0166000	*
870.7558_11.296	rel_abund_log2	b1	b3	12	12	-3.664656	11	0.0037200	**
871.6776_11.111	rel_abund_log2	b1	b3	12	12	-2.393305	11	0.0357000	*
871.6892_7.141	rel_abund_log2	b1	b3	12	12	2.817138	11	0.0168000	*
872.6526_8.477	rel_abund_log2	b1	b3	12	12	-2.430828	11	0.0334000	*
872.652_8.777	rel_abund_log2	b1	b3	12	12	-4.665764	11	0.0006870	***
872.7324_11.418	rel_abund_log2	b1	b3	12	12	-3.066165	11	0.0107000	*
872.7731_11.418	rel_abund_log2	b1	b3	12	12	-3.210925	11	0.0082900	**
873.7045_7.52	rel_abund_log2	b1	b3	12	12	-4.350664	11	0.0011500	**
875.7095_11.292	rel_abund_log2	b1	b3	12	12	-4.358804	11	0.0011400	**
875.7207_8.87	rel_abund_log2	b1	b3	12	12	2.710166	11	0.0203000	*
876.5695_7.88	rel_abund_log2	b1	b3	12	12	5.251274	11	0.0002720	***
876.6835_10.245	rel_abund_log2	b1	b3	12	12	-3.923586	11	0.0023800	**
877.6374_9.86	rel_abund_log2	b1	b3	12	12	3.355077	11	0.0064200	**
877.6374_9.988	rel_abund_log2	b1	b3	12	12	2.297251	11	0.0422000	*
877.7253_11.418	rel_abund_log2	b1	b3	12	12	2.829882	11	0.0164000	*
877.7_7.713	rel_abund_log2	b1	b3	12	12	3.139553	11	0.0094200	**
878.5852_8.857	rel_abund_log2	b1	b3	12	12	2.823743	11	0.0166000	*
880.718_11.248	rel_abund_log2	b1	b3	12	12	-3.720664	11	0.0033800	**
883.6892_7.419	rel_abund_log2	b1	b3	12	12	-5.011250	11	0.0003950	***
883.6894_6.894	rel_abund_log2	b1	b3	12	12	-2.429346	11	0.0334000	*
884.7697_11.358	rel_abund_log2	b1	b3	12	12	-2.602529	11	0.0246000	*
886.7858_11.492	rel_abund_log2	b1	b3	12	12	-2.573929	11	0.0259000	*
887.7201_8.662	rel_abund_log2	b1	b3	12	12	-2.553891	11	0.0268000	*
889.6375_9.586	rel_abund_log2	b1	b3	12	12	-4.152710	11	0.0016100	**
889.7358_8.865	rel_abund_log2	b1	b3	12	12	3.622630	11	0.0040100	**
891.7156_7.718	rel_abund_log2	b1	b3	12	12	2.354040	11	0.0382000	*
893.7563_11.645	rel_abund_log2	b1	b3	12	12	-3.038812	11	0.0113000	*
894.7548_11.21	rel_abund_log2	b1	b3	12	12	-2.829323	11	0.0164000	*
896.5379_6.373	rel_abund_log2	b1	b3	12	12	-3.011363	11	0.0118000	*
896.7714_11.316	rel_abund_log2	b1	b3	12	12	-2.859154	11	0.0155000	*
897.7047_7.419	rel_abund_log2	b1	b3	12	12	-7.460648	11	0.0000126	****
897.7049_6.893	rel_abund_log2	b1	b3	12	12	-2.303532	11	0.0418000	*
899.684_6.44	rel_abund_log2	b1	b3	12	12	-2.311324	11	0.0412000	*
899.7091_11.203	rel_abund_log2	b1	b3	12	12	-3.253770	11	0.0076800	**
899.7208_8.396	rel_abund_log2	b1	b3	12	12	-2.223400	11	0.0481000	*
901.6999_7.143	rel_abund_log2	b1	b3	12	12	2.770105	11	0.0182000	*
901.7251_11.318	rel_abund_log2	b1	b3	12	12	-2.593387	11	0.0250000	*
902.7673_8.957	rel_abund_log2	b1	b3	12	12	3.611847	11	0.0040800	**
902.8182_11.738	rel_abund_log2	b1	b3	12	12	5.192206	11	0.0002980	***
903.6531_10.207	rel_abund_log2	b1	b3	12	12	3.061865	11	0.0108000	*
903.6531_9.918	rel_abund_log2	b1	b3	12	12	-3.246741	11	0.0077800	**
904.7835_9.894	rel_abund_log2	b1	b3	12	12	2.454560	11	0.0320000	*
904.8336_11.91	rel_abund_log2	b1	b3	12	12	4.348976	11	0.0011600	**
907.7722_11.767	rel_abund_log2	b1	b3	12	12	2.326090	11	0.0401000	*
911.7206_7.958	rel_abund_log2	b1	b3	12	12	-2.392166	11	0.0357000	*
912.8727_11.58	rel_abund_log2	b1	b3	12	12	3.321919	11	0.0068100	**
913.7362_8.395	rel_abund_log2	b1	b3	12	12	-2.229213	11	0.0476000	*
915.7154_7.143	rel_abund_log2	b1	b3	12	12	3.229691	11	0.0080200	**
918.754_11.178	rel_abund_log2	b1	b3	12	12	-2.684068	11	0.0213000	*
918.7987_9.897	rel_abund_log2	b1	b3	12	12	5.778028	11	0.0001230	***
919.7465_8.87	rel_abund_log2	b1	b3	12	12	2.257752	11	0.0453000	*
923.684_6.374	rel_abund_log2	b1	b3	12	12	-3.239548	11	0.0078800	**
928.7831_8.964	rel_abund_log2	b1	b3	12	12	-2.273935	11	0.0440000	*
928.8306_11.753	rel_abund_log2	b1	b3	12	12	2.796540	11	0.0174000	*
930.7988_10.228	rel_abund_log2	b1	b3	12	12	4.972592	11	0.0004200	***
930.7989_9.903	rel_abund_log2	b1	b3	12	12	-3.423251	11	0.0056900	**
930.8479_11.919	rel_abund_log2	b1	b3	12	12	3.937493	11	0.0023200	**
937.6995_6.371	rel_abund_log2	b1	b3	12	12	-3.661694	11	0.0037400	**
938.8159_11.193	rel_abund_log2	b1	b3	12	12	-3.235770	11	0.0079300	**
941.7308_7.633	rel_abund_log2	b1	b3	12	12	-2.566482	11	0.0262000	*
941.7313_7.957	rel_abund_log2	b1	b3	12	12	-2.581726	11	0.0255000	*
944.7698_11.221	rel_abund_log2	b1	b3	12	12	-2.783268	11	0.0178000	*
944.8145_9.903	rel_abund_log2	b1	b3	12	12	-3.091203	11	0.0103000	*
949.7248_11.221	rel_abund_log2	b1	b3	12	12	-2.836895	11	0.0162000	*
951.7153_7.77	rel_abund_log2	b1	b3	12	12	-2.851466	11	0.0158000	*
953.7558_11.474	rel_abund_log2	b1	b3	12	12	2.569263	11	0.0261000	*
955.7465_8.781	rel_abund_log2	b1	b3	12	12	-2.674839	11	0.0216000	*
963.8345_12.135	rel_abund_log2	b1	b3	12	12	2.398651	11	0.0353000	*
965.7308_7.761	rel_abund_log2	b1	b3	12	12	-2.844707	11	0.0159000	*
966.8463_11.305	rel_abund_log2	b1	b3	12	12	-2.715736	11	0.0201000	*
972.7339_9.773	rel_abund_log2	b1	b3	12	12	3.024516	11	0.0116000	*
974.75_10.504	rel_abund_log2	b1	b3	12	12	2.903641	11	0.0144000	*
984.86_11.28	rel_abund_log2	b1	b3	12	12	2.263426	11	0.0448000	*
993.7984_10.238	rel_abund_log2	b1	b3	12	12	-3.136536	11	0.0094700	**

# how many are significant?
nrow(beta_t.test_paired_sig)

## [1] 592

Remove BG diet

# let's grab both datasets for features sig in pre v post beta and pre v post control and combine them
fulljoin_t.test_betaANDctrl <- full_join(beta_t.test_paired_sig, ctrl_t.test_paired_sig,
                                        by = "mz_rt",
                                        suffix = c(".beta", ".ctrl"))

# number of features in combined list
nrow(fulljoin_t.test_betaANDctrl)

## [1] 778

Let’s try and remove features significant due to the background diet

sig_paired_beta_rmBG <- fulljoin_t.test_betaANDctrl %>%
  # add a column to account for direction
  mutate(sign = statistic.beta * statistic.ctrl) %>%
  # replace NAs in the sign column with 0
  mutate(sign = replace_na(sign, 0)) %>%
  # replace NAs in the statistic.ctrl column to 0
  mutate(statistic.ctrl = replace_na(statistic.ctrl, 0)) %>%
  # filter for columns that are either negative (means ctrl and tomato are going in opposite dir) or where the stat.ctrl col is 0 (so we don't remove features that are just not present in the sig control list)
  filter((sign < 0 | statistic.ctrl == 0))

# number of features without bg diet effect
nrow(sig_paired_beta_rmBG)

## [1] 342

How many BG-diet-related features did we remove from the list of significant beta features?

nrow(beta_t.test_paired_sig) - nrow(sig_paired_beta_rmBG)

## [1] 250

ANOVA overlap

Keep sig features in t-test that have a match in sig ANOVA. Let’s take our new feature list (background diet effect removed)

# select only features from paired list that have a match in ANOVA list
beta_sig_ANOVA_overlap_paired <- inner_join(sig_paired_beta_rmBG,
                                            trt_tukeyHSD_sig,
                                            by = "mz_rt",
                                            suffix = c(".t-test", ".tukeys"))

# features overlapping with sig ANOVA
unique(beta_sig_ANOVA_overlap_paired$mz_rt)

##  [1] "1341.2177_11.895" "1342.2211_11.896" "1479.1185_7.024"  "1526.0908_8.976" 
##  [5] "1527.094_8.977"   "1536.6252_6.856"  "1594.0976_6.869"  "1618.2939_8.956" 
##  [9] "1619.2973_8.957"  "1649.3448_9.904"  "286.2013_0.633"   "337.1048_1.268"  
## [13] "536.437_11.201"   "605.5497_9.629"   "608.4637_5.649"   "677.5582_5.973"  
## [17] "677.5586_5.973"   "726.5428_8.237"   "738.543_8.181"    "748.5258_8.233"  
## [21] "755.4833_5.662"   "759.0903_6.983"   "759.1221_6.983"   "774.5404_8.982"  
## [25] "796.5222_9.007"   "797.6453_8.401"   "811.6598_8.009"   "813.6866_9.902"  
## [29] "827.6999_10.389"  "835.6566_8.968"   "851.64_9.902"     "865.5795_9.626"  
## [33] "876.5695_7.88"    "878.5852_8.857"   "889.6375_9.586"   "903.6531_10.207" 
## [37] "907.7722_11.767"  "974.75_10.504"

Paired red

# run paired t-tests for control intervention
red_t.test_paired <- df_for_stats %>%
  filter(treatment == "red") %>%
 dplyr::select(subject, period, mz_rt, rel_abund_log2) %>%
  group_by(mz_rt) %>%
  t_test(rel_abund_log2 ~ period, 
         paired = TRUE, 
         p.adjust.method = "BH") %>% # Benjamini-Hochberg controlling to lower false positives
  add_significance()

Statistically significant features

# which features are significant?
red_t.test_paired_sig <- red_t.test_paired %>%
  filter(p < 0.05)
kable(red_t.test_paired_sig)

mz_rt	.y.	group1	group2	n1	n2	statistic	df	p	p.signif
100.0757_0.629	rel_abund_log2	b1	b3	12	12	2.301791	11	0.0419000	*
1017.6873_3.095	rel_abund_log2	b1	b3	12	12	2.890974	11	0.0147000	*
1020.8931_11.508	rel_abund_log2	b1	b3	12	12	-2.210565	11	0.0492000	*
1022.9086_11.654	rel_abund_log2	b1	b3	12	12	-2.378913	11	0.0366000	*
1026.6606_3.098	rel_abund_log2	b1	b3	12	12	2.958742	11	0.0130000	*
1034.9093_11.511	rel_abund_log2	b1	b3	12	12	-2.505686	11	0.0292000	*
1036.925_11.654	rel_abund_log2	b1	b3	12	12	-5.164878	11	0.0003110	***
1039.6688_3.106	rel_abund_log2	b1	b3	12	12	2.746647	11	0.0190000	*
1041.9353_11.754	rel_abund_log2	b1	b3	12	12	-3.390033	11	0.0060300	**
1128.671_2.722	rel_abund_log2	b1	b3	12	12	2.292666	11	0.0426000	*
1151.3639_7.711	rel_abund_log2	b1	b3	12	12	4.321264	11	0.0012100	**
1151.8656_7.71	rel_abund_log2	b1	b3	12	12	6.583845	11	0.0000395	****
1172.8404_7.074	rel_abund_log2	b1	b3	12	12	2.445031	11	0.0325000	*
118.0864_0.597	rel_abund_log2	b1	b3	12	12	-7.527861	11	0.0000116	****
1193.9116_8.867	rel_abund_log2	b1	b3	12	12	3.062703	11	0.0108000	*
1199.3591_7.285	rel_abund_log2	b1	b3	12	12	-2.356886	11	0.0380000	*
1204.4035_8.865	rel_abund_log2	b1	b3	12	12	3.421241	11	0.0057100	**
1204.9038_8.864	rel_abund_log2	b1	b3	12	12	2.555463	11	0.0267000	*
1204.9948_9.897	rel_abund_log2	b1	b3	12	12	2.437899	11	0.0329000	*
1205.4054_8.863	rel_abund_log2	b1	b3	12	12	2.845936	11	0.0159000	*
1205.4975_9.898	rel_abund_log2	b1	b3	12	12	2.587322	11	0.0253000	*
1216.4896_9.892	rel_abund_log2	b1	b3	12	12	3.225108	11	0.0080900	**
1216.9911_9.896	rel_abund_log2	b1	b3	12	12	2.553121	11	0.0268000	*
1226.887_7.956	rel_abund_log2	b1	b3	12	12	-2.962983	11	0.0129000	*
1231.5117_9.905	rel_abund_log2	b1	b3	12	12	-2.757467	11	0.0186000	*
1237.8134_10.084	rel_abund_log2	b1	b3	12	12	2.509555	11	0.0290000	*
1242.5051_10.22	rel_abund_log2	b1	b3	12	12	2.601340	11	0.0246000	*
1242.5053_9.906	rel_abund_log2	b1	b3	12	12	-3.062344	11	0.0108000	*
1260.0333_9.997	rel_abund_log2	b1	b3	12	12	-2.490798	11	0.0300000	*
1281.8397_10.519	rel_abund_log2	b1	b3	12	12	2.307834	11	0.0415000	*
1282.9156_6.311	rel_abund_log2	b1	b3	12	12	2.215555	11	0.0487000	*
1285.8106_10.085	rel_abund_log2	b1	b3	12	12	-3.294039	11	0.0071500	**
1298.1748_11.858	rel_abund_log2	b1	b3	12	12	-2.402860	11	0.0351000	*
1339.0138_6.829	rel_abund_log2	b1	b3	12	12	4.904815	11	0.0004680	***
1341.0272_6.83	rel_abund_log2	b1	b3	12	12	5.333442	11	0.0002400	***
1363.015_6.835	rel_abund_log2	b1	b3	12	12	4.607274	11	0.0007560	***
1364.2059_11.753	rel_abund_log2	b1	b3	12	12	-2.219070	11	0.0484000	*
1365.0287_6.836	rel_abund_log2	b1	b3	12	12	5.621844	11	0.0001550	***
1389.0311_6.813	rel_abund_log2	b1	b3	12	12	5.627634	11	0.0001540	***
1424.1384_8.941	rel_abund_log2	b1	b3	12	12	-2.549831	11	0.0270000	*
1454.1867_9.904	rel_abund_log2	b1	b3	12	12	-4.676054	11	0.0006760	***
1454.1868_9.88	rel_abund_log2	b1	b3	12	12	-4.604527	11	0.0007590	***
1455.1905_9.914	rel_abund_log2	b1	b3	12	12	-3.161303	11	0.0090600	**
1473.1717_7.517	rel_abund_log2	b1	b3	12	12	-2.237906	11	0.0469000	*
1475.1518_7.004	rel_abund_log2	b1	b3	12	12	-3.334147	11	0.0066600	**
1476.1365_7.528	rel_abund_log2	b1	b3	12	12	-2.332112	11	0.0397000	*
1479.137_7.538	rel_abund_log2	b1	b3	12	12	-2.421904	11	0.0339000	*
1481.1402_7.792	rel_abund_log2	b1	b3	12	12	-2.648344	11	0.0227000	*
1487.1502_7.525	rel_abund_log2	b1	b3	12	12	-2.410813	11	0.0346000	*
1487.1515_6.871	rel_abund_log2	b1	b3	12	12	-2.258789	11	0.0452000	*
1497.1674_7.486	rel_abund_log2	b1	b3	12	12	-2.805313	11	0.0171000	*
1499.1861_7.555	rel_abund_log2	b1	b3	12	12	-2.469085	11	0.0312000	*
1500.1349_7.508	rel_abund_log2	b1	b3	12	12	-3.884271	11	0.0025400	**
1503.1775_7.514	rel_abund_log2	b1	b3	12	12	-2.888027	11	0.0148000	*
1506.089_7.132	rel_abund_log2	b1	b3	12	12	2.686665	11	0.0212000	*
1507.0917_7.135	rel_abund_log2	b1	b3	12	12	2.847409	11	0.0159000	*
1509.1328_6.475	rel_abund_log2	b1	b3	12	12	2.405750	11	0.0349000	*
1510.1552_7.549	rel_abund_log2	b1	b3	12	12	-2.889388	11	0.0147000	*
1511.145_6.854	rel_abund_log2	b1	b3	12	12	-3.647010	11	0.0038400	**
1512.1627_7.554	rel_abund_log2	b1	b3	12	12	-3.613675	11	0.0040700	**
1514.1517_7.514	rel_abund_log2	b1	b3	12	12	-2.795103	11	0.0174000	*
1515.1602_7.514	rel_abund_log2	b1	b3	12	12	-2.232318	11	0.0473000	*
1520.1651_7.717	rel_abund_log2	b1	b3	12	12	4.633900	11	0.0007240	***
1523.1825_7.536	rel_abund_log2	b1	b3	12	12	-2.931365	11	0.0137000	*
1524.1333_7.404	rel_abund_log2	b1	b3	12	12	-2.426715	11	0.0336000	*
1526.0908_8.976	rel_abund_log2	b1	b3	12	12	-2.374722	11	0.0368000	*
1527.094_8.977	rel_abund_log2	b1	b3	12	12	-2.299361	11	0.0421000	*
1531.181_7.771	rel_abund_log2	b1	b3	12	12	3.973347	11	0.0021800	**
1531.2124_8.09	rel_abund_log2	b1	b3	12	12	-2.512239	11	0.0289000	*
1531.6527_7.711	rel_abund_log2	b1	b3	12	12	4.109852	11	0.0017300	**
1532.1582_7.713	rel_abund_log2	b1	b3	12	12	5.739986	11	0.0001300	***
1534.154_7.527	rel_abund_log2	b1	b3	12	12	-2.363101	11	0.0376000	*
1535.1521_6.713	rel_abund_log2	b1	b3	12	12	-2.509670	11	0.0290000	*
1535.1558_7.533	rel_abund_log2	b1	b3	12	12	-4.135544	11	0.0016600	**
1538.1135_7.026	rel_abund_log2	b1	b3	12	12	3.418584	11	0.0057400	**
1538.1166_6.72	rel_abund_log2	b1	b3	12	12	2.323005	11	0.0404000	*
1538.1514_7.472	rel_abund_log2	b1	b3	12	12	-2.845147	11	0.0159000	*
1540.1625_7.542	rel_abund_log2	b1	b3	12	12	-2.267836	11	0.0445000	*
1541.1852_7.515	rel_abund_log2	b1	b3	12	12	-2.491987	11	0.0299000	*
1542.1448_7.703	rel_abund_log2	b1	b3	12	12	5.510543	11	0.0001830	***
1542.2142_6.931	rel_abund_log2	b1	b3	12	12	-2.799199	11	0.0173000	*
1542.645_7.698	rel_abund_log2	b1	b3	12	12	2.613145	11	0.0241000	*
1543.1481_7.702	rel_abund_log2	b1	b3	12	12	4.986808	11	0.0004110	***
1544.1549_7.686	rel_abund_log2	b1	b3	12	12	2.614583	11	0.0241000	*
1545.1606_7.669	rel_abund_log2	b1	b3	12	12	2.717844	11	0.0200000	*
1546.1714_7.763	rel_abund_log2	b1	b3	12	12	3.791085	11	0.0029900	**
1546.1749_7.78	rel_abund_log2	b1	b3	12	12	3.668422	11	0.0037000	**
1546.1772_8.02	rel_abund_log2	b1	b3	12	12	4.343516	11	0.0011700	**
1547.1793_7.789	rel_abund_log2	b1	b3	12	12	3.017012	11	0.0117000	*
1547.1803_7.856	rel_abund_log2	b1	b3	12	12	4.251943	11	0.0013600	**
1547.1806_8.02	rel_abund_log2	b1	b3	12	12	3.257466	11	0.0076300	**
1550.1462_7.021	rel_abund_log2	b1	b3	12	12	2.829346	11	0.0164000	*
1550.1483_7.401	rel_abund_log2	b1	b3	12	12	-2.718053	11	0.0200000	*
1552.1254_6.917	rel_abund_log2	b1	b3	12	12	2.422737	11	0.0338000	*
1557.1328_6.71	rel_abund_log2	b1	b3	12	12	-3.793241	11	0.0029800	**
1558.0818_8.319	rel_abund_log2	b1	b3	12	12	-3.016036	11	0.0117000	*
1558.1201_7.783	rel_abund_log2	b1	b3	12	12	-3.069525	11	0.0107000	*
1558.1357_6.713	rel_abund_log2	b1	b3	12	12	-4.115652	11	0.0017100	**
1561.1392_7.955	rel_abund_log2	b1	b3	12	12	-2.301668	11	0.0419000	*
1562.1488_7.418	rel_abund_log2	b1	b3	12	12	-4.808247	11	0.0005460	***
1562.1493_7.418	rel_abund_log2	b1	b3	12	12	-2.268444	11	0.0444000	*
1563.1535_7.418	rel_abund_log2	b1	b3	12	12	-5.880132	11	0.0001060	***
1563.2185_8.966	rel_abund_log2	b1	b3	12	12	-4.369573	11	0.0011200	**
1564.1662_7.508	rel_abund_log2	b1	b3	12	12	-4.151074	11	0.0016100	**
1572.1304_7.394	rel_abund_log2	b1	b3	12	12	-3.013713	11	0.0118000	*
1574.1454_7.378	rel_abund_log2	b1	b3	12	12	-4.603838	11	0.0007600	***
1574.2638_8.923	rel_abund_log2	b1	b3	12	12	2.637301	11	0.0231000	*
1576.1634_7.391	rel_abund_log2	b1	b3	12	12	-2.440638	11	0.0328000	*
1577.1679_7.401	rel_abund_log2	b1	b3	12	12	-2.237609	11	0.0469000	*
1579.649_7.139	rel_abund_log2	b1	b3	12	12	2.706296	11	0.0204000	*
1580.1032_7.792	rel_abund_log2	b1	b3	12	12	-4.388308	11	0.0010800	**
1580.9848_2.737	rel_abund_log2	b1	b3	12	12	2.466614	11	0.0313000	*
1581.1632_7.283	rel_abund_log2	b1	b3	12	12	-2.447767	11	0.0324000	*
1585.1303_6.711	rel_abund_log2	b1	b3	12	12	-2.895328	11	0.0146000	*
1585.2008_8.971	rel_abund_log2	b1	b3	12	12	-7.458777	11	0.0000126	****
1585.6318_6.711	rel_abund_log2	b1	b3	12	12	-2.240342	11	0.0467000	*
1586.1131_6.864	rel_abund_log2	b1	b3	12	12	-2.569585	11	0.0261000	*
1586.1251_6.718	rel_abund_log2	b1	b3	12	12	-2.458546	11	0.0318000	*
1586.2043_8.972	rel_abund_log2	b1	b3	12	12	-6.216002	11	0.0000657	****
1588.1988_7.697	rel_abund_log2	b1	b3	12	12	-2.419063	11	0.0341000	*
1589.2021_7.7	rel_abund_log2	b1	b3	12	12	-2.230743	11	0.0475000	*
1590.1749_7.958	rel_abund_log2	b1	b3	12	12	-3.689518	11	0.0035700	**
1591.1826_7.956	rel_abund_log2	b1	b3	12	12	-3.766072	11	0.0031200	**
1592.1507_6.918	rel_abund_log2	b1	b3	12	12	-2.438041	11	0.0329000	*
1596.3093_9.895	rel_abund_log2	b1	b3	12	12	2.284502	11	0.0432000	*
1596.6247_6.715	rel_abund_log2	b1	b3	12	12	-2.577322	11	0.0257000	*
1597.3131_9.894	rel_abund_log2	b1	b3	12	12	3.955483	11	0.0022500	**
1598.1451_7.197	rel_abund_log2	b1	b3	12	12	-2.675640	11	0.0216000	*
1600.6232_6.854	rel_abund_log2	b1	b3	12	12	-2.733768	11	0.0194000	*
1604.1948_7.675	rel_abund_log2	b1	b3	12	12	-2.547252	11	0.0271000	*
1605.1979_7.674	rel_abund_log2	b1	b3	12	12	-3.015505	11	0.0117000	*
1606.2002_7.968	rel_abund_log2	b1	b3	12	12	-2.217449	11	0.0486000	*
1610.1103_6.827	rel_abund_log2	b1	b3	12	12	-2.431077	11	0.0333000	*
1610.32_9.892	rel_abund_log2	b1	b3	12	12	2.324247	11	0.0403000	*
1617.1626_7.754	rel_abund_log2	b1	b3	12	12	-4.709793	11	0.0006400	***
1621.1991_7.959	rel_abund_log2	b1	b3	12	12	-3.603673	11	0.0041400	**
1621.2757_7.96	rel_abund_log2	b1	b3	12	12	-2.513383	11	0.0288000	*
1629.2009_7.725	rel_abund_log2	b1	b3	12	12	-3.864659	11	0.0026300	**
1631.1866_7.959	rel_abund_log2	b1	b3	12	12	-4.510912	11	0.0008850	***
1632.1881_7.956	rel_abund_log2	b1	b3	12	12	-3.464748	11	0.0052900	**
1646.2057_7.997	rel_abund_log2	b1	b3	12	12	-3.760727	11	0.0031500	**
1647.2105_8.001	rel_abund_log2	b1	b3	12	12	-3.627605	11	0.0039700	**
1648.3413_9.902	rel_abund_log2	b1	b3	12	12	-2.795073	11	0.0174000	*
1649.3444_10.225	rel_abund_log2	b1	b3	12	12	3.468146	11	0.0052600	**
1649.3448_9.904	rel_abund_log2	b1	b3	12	12	-2.770484	11	0.0182000	*
205.1221_0.621	rel_abund_log2	b1	b3	12	12	7.203038	11	0.0000175	****
286.1437_1.13	rel_abund_log2	b1	b3	12	12	-4.552370	11	0.0008270	***
293.0984_0.628	rel_abund_log2	b1	b3	12	12	4.371464	11	0.0011100	**
308.1855_0.633	rel_abund_log2	b1	b3	12	12	-11.692784	11	0.0000002	****
312.1597_1.413	rel_abund_log2	b1	b3	12	12	-6.243695	11	0.0000632	****
315.0805_0.637	rel_abund_log2	b1	b3	12	12	2.291372	11	0.0427000	*
331.0535_0.634	rel_abund_log2	b1	b3	12	12	4.536381	11	0.0008490	***
373.2483_1.147	rel_abund_log2	b1	b3	12	12	-4.348920	11	0.0011600	**
464.1914_0.656	rel_abund_log2	b1	b3	12	12	-3.787295	11	0.0030100	**
480.3082_3.357	rel_abund_log2	b1	b3	12	12	3.440297	11	0.0055200	**
494.3238_2.494	rel_abund_log2	b1	b3	12	12	2.946906	11	0.0133000	*
502.2902_3.355	rel_abund_log2	b1	b3	12	12	2.731487	11	0.0195000	*
508.3393_2.879	rel_abund_log2	b1	b3	12	12	2.540528	11	0.0275000	*
519.3249_3.064	rel_abund_log2	b1	b3	12	12	-3.069860	11	0.0107000	*
522.3564_3.24	rel_abund_log2	b1	b3	12	12	3.917152	11	0.0024000	**
526.2644_1.714	rel_abund_log2	b1	b3	12	12	-6.892585	11	0.0000261	****
536.437_10.971	rel_abund_log2	b1	b3	12	12	-8.537284	11	0.0000035	****
542.3237_2.234	rel_abund_log2	b1	b3	12	12	3.173620	11	0.0088600	**
543.4017_4.622	rel_abund_log2	b1	b3	12	12	2.659865	11	0.0222000	*
544.3374_3.238	rel_abund_log2	b1	b3	12	12	3.479794	11	0.0051500	**
550.3862_3.859	rel_abund_log2	b1	b3	12	12	2.446736	11	0.0324000	*
552.4018_4.301	rel_abund_log2	b1	b3	12	12	2.608580	11	0.0243000	*
563.4273_4.415	rel_abund_log2	b1	b3	12	12	2.723593	11	0.0198000	*
568.4266_4.64	rel_abund_log2	b1	b3	12	12	-3.536226	11	0.0046600	**
577.5185_8.358	rel_abund_log2	b1	b3	12	12	4.022549	11	0.0020100	**
578.3923_2.734	rel_abund_log2	b1	b3	12	12	2.679200	11	0.0214000	*
593.4757_4.54	rel_abund_log2	b1	b3	12	12	6.135627	11	0.0000736	****
593.4762_4.644	rel_abund_log2	b1	b3	12	12	6.083887	11	0.0000792	****
593.4768_4.312	rel_abund_log2	b1	b3	12	12	4.385799	11	0.0010900	**
595.4929_4.645	rel_abund_log2	b1	b3	12	12	8.777957	11	0.0000027	****
596.4154_4.906	rel_abund_log2	b1	b3	12	12	-5.670589	11	0.0001440	***
597.5086_5.242	rel_abund_log2	b1	b3	12	12	7.478443	11	0.0000123	****
605.5497_9.629	rel_abund_log2	b1	b3	12	12	5.315001	11	0.0002470	***
610.431_5.16	rel_abund_log2	b1	b3	12	12	-2.749583	11	0.0189000	*
610.5401_9.655	rel_abund_log2	b1	b3	12	12	-2.825339	11	0.0165000	*
610.5401_9.784	rel_abund_log2	b1	b3	12	12	-2.970910	11	0.0127000	*
617.4748_4.643	rel_abund_log2	b1	b3	12	12	4.498395	11	0.0009040	***
619.4811_4.462	rel_abund_log2	b1	b3	12	12	2.264179	11	0.0448000	*
619.4903_4.953	rel_abund_log2	b1	b3	12	12	7.113931	11	0.0000196	****
623.1355_0.614	rel_abund_log2	b1	b3	12	12	4.066781	11	0.0018600	**
629.1522_0.633	rel_abund_log2	b1	b3	12	12	3.148581	11	0.0092700	**
634.5399_9.061	rel_abund_log2	b1	b3	12	12	-5.152931	11	0.0003170	***
634.54_8.941	rel_abund_log2	b1	b3	12	12	-5.092662	11	0.0003480	***
636.5558_9.845	rel_abund_log2	b1	b3	12	12	-2.622446	11	0.0237000	*
636.5558_9.995	rel_abund_log2	b1	b3	12	12	-2.452395	11	0.0321000	*
643.38_0.69	rel_abund_log2	b1	b3	12	12	-3.108471	11	0.0099500	**
645.1222_0.627	rel_abund_log2	b1	b3	12	12	4.888460	11	0.0004800	***
646.6126_10.526	rel_abund_log2	b1	b3	12	12	-2.839848	11	0.0161000	*
650.6449_10.83	rel_abund_log2	b1	b3	12	12	2.802311	11	0.0172000	*
690.6188_11.754	rel_abund_log2	b1	b3	12	12	-2.215897	11	0.0487000	*
691.5742_6.403	rel_abund_log2	b1	b3	12	12	-2.407718	11	0.0348000	*
704.3938_6.501	rel_abund_log2	b1	b3	12	12	2.379313	11	0.0365000	*
717.5903_7.018	rel_abund_log2	b1	b3	12	12	-3.061996	11	0.0108000	*
718.5742_8.138	rel_abund_log2	b1	b3	12	12	-2.361426	11	0.0377000	*
719.6006_7.392	rel_abund_log2	b1	b3	12	12	-5.082736	11	0.0003530	***
724.5277_7.806	rel_abund_log2	b1	b3	12	12	-2.447396	11	0.0324000	*
727.5745_6.067	rel_abund_log2	b1	b3	12	12	-2.687826	11	0.0211000	*
729.5912_6.714	rel_abund_log2	b1	b3	12	12	-3.482661	11	0.0051200	**
732.5542_6.723	rel_abund_log2	b1	b3	12	12	2.709267	11	0.0203000	*
732.589_8.53	rel_abund_log2	b1	b3	12	12	-2.219182	11	0.0484000	*
738.5064_6.603	rel_abund_log2	b1	b3	12	12	2.495743	11	0.0297000	*
738.543_8.181	rel_abund_log2	b1	b3	12	12	-4.252257	11	0.0013600	**
740.5559_8.156	rel_abund_log2	b1	b3	12	12	-2.840597	11	0.0161000	*
742.5753_7.523	rel_abund_log2	b1	b3	12	12	-2.705037	11	0.0205000	*
743.6054_7.227	rel_abund_log2	b1	b3	12	12	-5.919879	11	0.0001000	****
745.6202_7.852	rel_abund_log2	b1	b3	12	12	-3.131762	11	0.0095500	**
745.6214_8.115	rel_abund_log2	b1	b3	12	12	-2.429825	11	0.0334000	*
746.5091_7.814	rel_abund_log2	b1	b3	12	12	-2.925768	11	0.0138000	*
748.5258_8.233	rel_abund_log2	b1	b3	12	12	-2.217273	11	0.0486000	*
749.5557_6.066	rel_abund_log2	b1	b3	12	12	-3.086577	11	0.0103000	*
751.5717_6.812	rel_abund_log2	b1	b3	12	12	-2.274171	11	0.0440000	*
752.5592_8.969	rel_abund_log2	b1	b3	12	12	-2.222425	11	0.0482000	*
752.5594_8.973	rel_abund_log2	b1	b3	12	12	-2.220889	11	0.0483000	*
755.6054_6.838	rel_abund_log2	b1	b3	12	12	-3.504610	11	0.0049300	**
759.4494_6.981	rel_abund_log2	b1	b3	12	12	-2.841271	11	0.0160000	*
760.421_6.983	rel_abund_log2	b1	b3	12	12	2.642327	11	0.0229000	*
760.4925_6.98	rel_abund_log2	b1	b3	12	12	-2.322611	11	0.0404000	*
760.5281_6.984	rel_abund_log2	b1	b3	12	12	3.151766	11	0.0092100	**
760.5406_6.984	rel_abund_log2	b1	b3	12	12	-3.946902	11	0.0022800	**
760.5459_7.719	rel_abund_log2	b1	b3	12	12	2.796156	11	0.0174000	*
760.5883_7.719	rel_abund_log2	b1	b3	12	12	6.299590	11	0.0000584	****
760.9763_7.718	rel_abund_log2	b1	b3	12	12	3.532211	11	0.0047000	**
761.1927_7.717	rel_abund_log2	b1	b3	12	12	3.191564	11	0.0085800	**
761.2595_7.716	rel_abund_log2	b1	b3	12	12	3.658534	11	0.0037600	**
761.302_7.716	rel_abund_log2	b1	b3	12	12	5.097205	11	0.0003460	***
761.3174_7.717	rel_abund_log2	b1	b3	12	12	2.686113	11	0.0212000	*
761.329_7.717	rel_abund_log2	b1	b3	12	12	3.269784	11	0.0074700	**
761.3681_7.718	rel_abund_log2	b1	b3	12	12	4.318854	11	0.0012200	**
761.3781_7.717	rel_abund_log2	b1	b3	12	12	3.707556	11	0.0034600	**
761.4062_7.717	rel_abund_log2	b1	b3	12	12	3.224083	11	0.0081000	**
761.4141_7.718	rel_abund_log2	b1	b3	12	12	4.273631	11	0.0013100	**
761.4437_7.717	rel_abund_log2	b1	b3	12	12	3.590018	11	0.0042400	**
761.4545_7.719	rel_abund_log2	b1	b3	12	12	4.354470	11	0.0011500	**
761.5191_7.716	rel_abund_log2	b1	b3	12	12	4.686045	11	0.0006650	***
762.5032_7.719	rel_abund_log2	b1	b3	12	12	5.043618	11	0.0003760	***
762.5251_7.716	rel_abund_log2	b1	b3	12	12	3.202730	11	0.0084100	**
762.5433_7.717	rel_abund_log2	b1	b3	12	12	3.514086	11	0.0048500	**
764.5195_7.458	rel_abund_log2	b1	b3	12	12	2.206216	11	0.0495000	*
766.5713_8.323	rel_abund_log2	b1	b3	12	12	3.863274	11	0.0026400	**
766.5755_7.417	rel_abund_log2	b1	b3	12	12	-4.833479	11	0.0005240	***
768.551_7.206	rel_abund_log2	b1	b3	12	12	3.182754	11	0.0087200	**
768.5536_6.434	rel_abund_log2	b1	b3	12	12	-2.415815	11	0.0343000	*
768.5859_8.446	rel_abund_log2	b1	b3	12	12	2.812600	11	0.0169000	*
768.587_8.451	rel_abund_log2	b1	b3	12	12	2.670088	11	0.0218000	*
768.5909_7.579	rel_abund_log2	b1	b3	12	12	-2.539628	11	0.0275000	*
771.5762_7.712	rel_abund_log2	b1	b3	12	12	4.427648	11	0.0010200	**
773.6516_9.025	rel_abund_log2	b1	b3	12	12	-3.923204	11	0.0023800	**
780.5549_6.325	rel_abund_log2	b1	b3	12	12	2.265280	11	0.0447000	*
780.5921_7.79	rel_abund_log2	b1	b3	12	12	-4.047844	11	0.0019200	**
781.6192_8.718	rel_abund_log2	b1	b3	12	12	3.315082	11	0.0068900	**
781.62_7.043	rel_abund_log2	b1	b3	12	12	-5.517666	11	0.0001810	***
783.2313_6.877	rel_abund_log2	b1	b3	12	12	-2.495940	11	0.0297000	*
783.6368_7.847	rel_abund_log2	b1	b3	12	12	-6.184435	11	0.0000687	****
784.5879_7.141	rel_abund_log2	b1	b3	12	12	2.216599	11	0.0487000	*
784.6654_10.38	rel_abund_log2	b1	b3	12	12	2.642904	11	0.0229000	*
784.779_6.877	rel_abund_log2	b1	b3	12	12	-2.422941	11	0.0338000	*
785.0064_6.878	rel_abund_log2	b1	b3	12	12	-2.620589	11	0.0238000	*
785.0201_6.879	rel_abund_log2	b1	b3	12	12	-2.603001	11	0.0246000	*
785.0389_6.88	rel_abund_log2	b1	b3	12	12	-2.676730	11	0.0215000	*
785.6536_8.747	rel_abund_log2	b1	b3	12	12	-2.897012	11	0.0145000	*
786.5814_6.876	rel_abund_log2	b1	b3	12	12	-3.294990	11	0.0071400	**
787.6674_9.202	rel_abund_log2	b1	b3	12	12	-4.800036	11	0.0005530	***
787.6701_9.896	rel_abund_log2	b1	b3	12	12	2.369448	11	0.0372000	*
788.5563_7.415	rel_abund_log2	b1	b3	12	12	-6.413750	11	0.0000498	****
788.6177_8.867	rel_abund_log2	b1	b3	12	12	2.487333	11	0.0302000	*
790.5744_7.193	rel_abund_log2	b1	b3	12	12	-3.924007	11	0.0023800	**
791.5433_6.743	rel_abund_log2	b1	b3	12	12	3.325197	11	0.0067700	**
792.5531_6.261	rel_abund_log2	b1	b3	12	12	-2.644635	11	0.0228000	*
792.5534_8.102	rel_abund_log2	b1	b3	12	12	-2.337666	11	0.0393000	*
792.5893_7.058	rel_abund_log2	b1	b3	12	12	3.020604	11	0.0116000	*
792.5899_7.556	rel_abund_log2	b1	b3	12	12	-2.958261	11	0.0130000	*
794.6054_8.214	rel_abund_log2	b1	b3	12	12	-3.720625	11	0.0033800	**
794.6057_8.544	rel_abund_log2	b1	b3	12	12	-2.235984	11	0.0470000	*
794.6062_7.691	rel_abund_log2	b1	b3	12	12	-2.433692	11	0.0332000	*
796.5845_7.27	rel_abund_log2	b1	b3	12	12	-2.742574	11	0.0191000	*
796.5848_7.423	rel_abund_log2	b1	b3	12	12	-4.620937	11	0.0007390	***
796.621_8.383	rel_abund_log2	b1	b3	12	12	-2.456822	11	0.0319000	*
797.6383_8.603	rel_abund_log2	b1	b3	12	12	-3.389415	11	0.0060400	**
797.6453_8.401	rel_abund_log2	b1	b3	12	12	-4.778933	11	0.0005720	***
798.5402_7.73	rel_abund_log2	b1	b3	12	12	2.734966	11	0.0194000	*
798.6003_7.83	rel_abund_log2	b1	b3	12	12	-2.531295	11	0.0279000	*
798.6518_8.391	rel_abund_log2	b1	b3	12	12	-5.954199	11	0.0000953	****
799.6069_8.865	rel_abund_log2	b1	b3	12	12	2.294520	11	0.0424000	*
799.6686_9.32	rel_abund_log2	b1	b3	12	12	-4.160291	11	0.0015900	**
802.5357_6.323	rel_abund_log2	b1	b3	12	12	2.458205	11	0.0318000	*
802.5717_7.81	rel_abund_log2	b1	b3	12	12	-3.075678	11	0.0106000	*
804.5937_7.544	rel_abund_log2	b1	b3	12	12	-2.568849	11	0.0261000	*
805.0108_3.238	rel_abund_log2	b1	b3	12	12	3.622544	11	0.0040100	**
805.6187_7.848	rel_abund_log2	b1	b3	12	12	-4.117799	11	0.0017100	**
805.6784_9.291	rel_abund_log2	b1	b3	12	12	2.919403	11	0.0140000	*
807.6349_8.948	rel_abund_log2	b1	b3	12	12	4.663819	11	0.0006890	***
807.6359_7.483	rel_abund_log2	b1	b3	12	12	-5.110564	11	0.0003380	***
808.6375_8.751	rel_abund_log2	b1	b3	12	12	-3.287272	11	0.0072400	**
809.6507_9.876	rel_abund_log2	b1	b3	12	12	3.399918	11	0.0059300	**
809.6525_8.065	rel_abund_log2	b1	b3	12	12	-4.032616	11	0.0019700	**
810.5988_8.865	rel_abund_log2	b1	b3	12	12	2.584515	11	0.0254000	*
810.6013_7.646	rel_abund_log2	b1	b3	12	12	-2.294571	11	0.0424000	*
810.6041_7.959	rel_abund_log2	b1	b3	12	12	-3.003128	11	0.0120000	*
810.6564_8.03	rel_abund_log2	b1	b3	12	12	-3.611987	11	0.0040800	**
810.6812_10.387	rel_abund_log2	b1	b3	12	12	-3.071393	11	0.0106000	*
811.0052_7.959	rel_abund_log2	b1	b3	12	12	-2.611903	11	0.0242000	*
811.2977_7.959	rel_abund_log2	b1	b3	12	12	-2.473330	11	0.0309000	*
811.4149_7.961	rel_abund_log2	b1	b3	12	12	-2.720729	11	0.0199000	*
811.4537_7.958	rel_abund_log2	b1	b3	12	12	-2.595710	11	0.0249000	*
811.5374_7.96	rel_abund_log2	b1	b3	12	12	-2.338350	11	0.0393000	*
811.6292_7.819	rel_abund_log2	b1	b3	12	12	-3.317591	11	0.0068600	**
811.6703_8.961	rel_abund_log2	b1	b3	12	12	-3.477834	11	0.0051700	**
812.5561_7.198	rel_abund_log2	b1	b3	12	12	-3.677711	11	0.0036400	**
813.6106_7.959	rel_abund_log2	b1	b3	12	12	-2.453038	11	0.0321000	*
813.6834_9.437	rel_abund_log2	b1	b3	12	12	-4.615418	11	0.0007460	***
813.6866_9.902	rel_abund_log2	b1	b3	12	12	-2.406519	11	0.0348000	*
814.5355_8.122	rel_abund_log2	b1	b3	12	12	-3.224469	11	0.0080900	**
814.535_6.26	rel_abund_log2	b1	b3	12	12	-2.251281	11	0.0458000	*
814.6127_7.96	rel_abund_log2	b1	b3	12	12	-2.496902	11	0.0297000	*
815.7025_10.568	rel_abund_log2	b1	b3	12	12	2.795121	11	0.0174000	*
816.5872_8.538	rel_abund_log2	b1	b3	12	12	-2.587620	11	0.0252000	*
817.7051_10.344	rel_abund_log2	b1	b3	12	12	-3.133261	11	0.0095200	**
818.5668_7.42	rel_abund_log2	b1	b3	12	12	-3.005333	11	0.0120000	*
818.6049_7.793	rel_abund_log2	b1	b3	12	12	-3.303713	11	0.0070300	**
819.6577_6.728	rel_abund_log2	b1	b3	12	12	3.282880	11	0.0073000	**
820.5836_6.837	rel_abund_log2	b1	b3	12	12	-2.769597	11	0.0182000	*
820.5846_7.213	rel_abund_log2	b1	b3	12	12	-3.709193	11	0.0034500	**
820.6202_7.907	rel_abund_log2	b1	b3	12	12	-6.565304	11	0.0000405	****
820.6961_11.574	rel_abund_log2	b1	b3	12	12	-6.272267	11	0.0000607	****
822.5994_7.509	rel_abund_log2	b1	b3	12	12	-5.140725	11	0.0003230	***
824.616_8.544	rel_abund_log2	b1	b3	12	12	-2.352586	11	0.0383000	*
824.6209_7.408	rel_abund_log2	b1	b3	12	12	-3.452299	11	0.0054100	**
824.6527_9.167	rel_abund_log2	b1	b3	12	12	-2.284485	11	0.0432000	*
825.6241_9.886	rel_abund_log2	b1	b3	12	12	2.513064	11	0.0288000	*
825.6473_7.81	rel_abund_log2	b1	b3	12	12	-2.659947	11	0.0222000	*
825.6778_9.575	rel_abund_log2	b1	b3	12	12	-4.265566	11	0.0013300	**
825.6815_9.335	rel_abund_log2	b1	b3	12	12	-9.279000	11	0.0000016	****
825.682_9.561	rel_abund_log2	b1	b3	12	12	-3.589082	11	0.0042500	**
826.5716_8.868	rel_abund_log2	b1	b3	12	12	2.366507	11	0.0374000	*
827.663_8.483	rel_abund_log2	b1	b3	12	12	-4.978174	11	0.0004170	***
827.6998_10.277	rel_abund_log2	b1	b3	12	12	-6.660916	11	0.0000356	****
827.6999_10.389	rel_abund_log2	b1	b3	12	12	-5.659058	11	0.0001470	***
827.6999_10.502	rel_abund_log2	b1	b3	12	12	-2.520035	11	0.0285000	*
828.5522_5.728	rel_abund_log2	b1	b3	12	12	2.402752	11	0.0351000	*
828.6106_7.766	rel_abund_log2	b1	b3	12	12	-2.334177	11	0.0396000	*
829.6782_8.401	rel_abund_log2	b1	b3	12	12	-4.322869	11	0.0012100	**
830.6471_9.551	rel_abund_log2	b1	b3	12	12	-3.536399	11	0.0046600	**
831.57_7.208	rel_abund_log2	b1	b3	12	12	-2.348517	11	0.0386000	*
831.6353_8.196	rel_abund_log2	b1	b3	12	12	-5.429552	11	0.0002070	***
832.582_7.631	rel_abund_log2	b1	b3	12	12	-3.581294	11	0.0043100	**
834.6002_7.257	rel_abund_log2	b1	b3	12	12	-4.639789	11	0.0007170	***
834.602_7.751	rel_abund_log2	b1	b3	12	12	-2.958872	11	0.0130000	*
835.6663_8.407	rel_abund_log2	b1	b3	12	12	-3.459352	11	0.0053400	**
835.6664_10.22	rel_abund_log2	b1	b3	12	12	2.495114	11	0.0298000	*
835.6666_9.91	rel_abund_log2	b1	b3	12	12	-3.947588	11	0.0022800	**
836.6148_8.439	rel_abund_log2	b1	b3	12	12	-2.202123	11	0.0499000	*
837.5477_8.363	rel_abund_log2	b1	b3	12	12	4.567409	11	0.0008070	***
837.6724_9.906	rel_abund_log2	b1	b3	12	12	-4.533839	11	0.0008520	***
839.6989_10.178	rel_abund_log2	b1	b3	12	12	-2.815759	11	0.0168000	*
842.638_7.268	rel_abund_log2	b1	b3	12	12	-2.546429	11	0.0272000	*
842.723_11.155	rel_abund_log2	b1	b3	12	12	-2.294663	11	0.0424000	*
844.7397_11.265	rel_abund_log2	b1	b3	12	12	-2.409950	11	0.0346000	*
846.6641_8.878	rel_abund_log2	b1	b3	12	12	2.287845	11	0.0429000	*
847.6893_7.716	rel_abund_log2	b1	b3	12	12	3.521152	11	0.0047900	**
848.6521_9.028	rel_abund_log2	b1	b3	12	12	-5.509323	11	0.0001840	***
849.6682_6.746	rel_abund_log2	b1	b3	12	12	2.894409	11	0.0146000	*
854.4963_2.738	rel_abund_log2	b1	b3	12	12	2.448216	11	0.0323000	*
854.574_8.362	rel_abund_log2	b1	b3	12	12	6.157517	11	0.0000713	****
855.6216_6.322	rel_abund_log2	b1	b3	12	12	2.427139	11	0.0336000	*
856.5819_7.24	rel_abund_log2	b1	b3	12	12	-5.707520	11	0.0001370	***
856.5823_7.778	rel_abund_log2	b1	b3	12	12	-3.750779	11	0.0032100	**
858.5914_7.792	rel_abund_log2	b1	b3	12	12	-3.237207	11	0.0079100	**
858.5977_8.453	rel_abund_log2	b1	b3	12	12	-2.993840	11	0.0122000	*
858.5992_7.06	rel_abund_log2	b1	b3	12	12	-3.983599	11	0.0021500	**
858.7545_11.345	rel_abund_log2	b1	b3	12	12	-3.056433	11	0.0109000	*
859.53_8.35	rel_abund_log2	b1	b3	12	12	4.511522	11	0.0008840	***
859.6891_7.519	rel_abund_log2	b1	b3	12	12	-3.081250	11	0.0104000	*
860.6133_8.785	rel_abund_log2	b1	b3	12	12	-2.609245	11	0.0243000	*
861.7048_7.713	rel_abund_log2	b1	b3	12	12	3.925017	11	0.0023700	**
863.6838_8.874	rel_abund_log2	b1	b3	12	12	2.856332	11	0.0156000	*
863.6839_6.733	rel_abund_log2	b1	b3	12	12	2.684970	11	0.0212000	*
863.7092_11.343	rel_abund_log2	b1	b3	12	12	-2.920883	11	0.0139000	*
865.5795_9.626	rel_abund_log2	b1	b3	12	12	4.358050	11	0.0011400	**
868.7389_11.19	rel_abund_log2	b1	b3	12	12	-2.911197	11	0.0142000	*
870.6336_9.039	rel_abund_log2	b1	b3	12	12	-6.403492	11	0.0000506	****
870.7558_11.296	rel_abund_log2	b1	b3	12	12	-4.128270	11	0.0016800	**
871.6776_11.111	rel_abund_log2	b1	b3	12	12	-2.208492	11	0.0493000	*
872.5379_6.428	rel_abund_log2	b1	b3	12	12	-2.370879	11	0.0371000	*
872.6526_8.477	rel_abund_log2	b1	b3	12	12	-4.693492	11	0.0006570	***
872.652_8.777	rel_abund_log2	b1	b3	12	12	-3.936031	11	0.0023300	**
872.7731_11.418	rel_abund_log2	b1	b3	12	12	-4.053025	11	0.0019100	**
873.7045_7.52	rel_abund_log2	b1	b3	12	12	-2.451144	11	0.0322000	*
874.6655_10.042	rel_abund_log2	b1	b3	12	12	-7.151698	11	0.0000186	****
874.7461_11.559	rel_abund_log2	b1	b3	12	12	-2.257252	11	0.0453000	*
875.7095_11.292	rel_abund_log2	b1	b3	12	12	-3.562920	11	0.0044500	**
875.7207_8.87	rel_abund_log2	b1	b3	12	12	2.365050	11	0.0375000	*
876.5695_7.88	rel_abund_log2	b1	b3	12	12	2.286973	11	0.0430000	*
876.6835_10.245	rel_abund_log2	b1	b3	12	12	-6.880155	11	0.0000266	****
876.6836_10.537	rel_abund_log2	b1	b3	12	12	-2.315502	11	0.0409000	*
876.854_11.56	rel_abund_log2	b1	b3	12	12	-2.619942	11	0.0238000	*
877.6374_9.86	rel_abund_log2	b1	b3	12	12	3.866733	11	0.0026200	**
877.6374_9.988	rel_abund_log2	b1	b3	12	12	2.620523	11	0.0238000	*
877.7_7.713	rel_abund_log2	b1	b3	12	12	2.564642	11	0.0263000	*
878.5852_8.857	rel_abund_log2	b1	b3	12	12	2.279151	11	0.0436000	*
878.7033_7.717	rel_abund_log2	b1	b3	12	12	6.785827	11	0.0000301	****
879.7408_11.578	rel_abund_log2	b1	b3	12	12	2.505650	11	0.0292000	*
880.718_11.248	rel_abund_log2	b1	b3	12	12	-2.511851	11	0.0289000	*
883.6892_7.419	rel_abund_log2	b1	b3	12	12	-5.423001	11	0.0002090	***
883.6894_6.894	rel_abund_log2	b1	b3	12	12	-2.453436	11	0.0320000	*
884.7697_11.358	rel_abund_log2	b1	b3	12	12	-3.945413	11	0.0022900	**
885.7047_7.587	rel_abund_log2	b1	b3	12	12	-2.770862	11	0.0182000	*
886.7858_11.492	rel_abund_log2	b1	b3	12	12	-3.577744	11	0.0043400	**
891.7156_7.718	rel_abund_log2	b1	b3	12	12	4.087962	11	0.0018000	**
891.7406_11.489	rel_abund_log2	b1	b3	12	12	-3.302745	11	0.0070400	**
892.7383_11.126	rel_abund_log2	b1	b3	12	12	-2.819218	11	0.0167000	*
894.7548_11.21	rel_abund_log2	b1	b3	12	12	-3.405735	11	0.0058700	**
896.7714_11.316	rel_abund_log2	b1	b3	12	12	-3.720716	11	0.0033800	**
897.7047_7.419	rel_abund_log2	b1	b3	12	12	-4.718995	11	0.0006300	***
897.705_7.958	rel_abund_log2	b1	b3	12	12	-3.091411	11	0.0103000	*
898.7872_11.44	rel_abund_log2	b1	b3	12	12	-2.438121	11	0.0329000	*
899.7091_11.203	rel_abund_log2	b1	b3	12	12	-4.549683	11	0.0008310	***
900.6836_9.96	rel_abund_log2	b1	b3	12	12	-4.963738	11	0.0004260	***
901.6999_7.143	rel_abund_log2	b1	b3	12	12	3.762940	11	0.0031400	**
901.7251_11.318	rel_abund_log2	b1	b3	12	12	-2.665574	11	0.0220000	*
907.6886_7.2	rel_abund_log2	b1	b3	12	12	-2.370170	11	0.0371000	*
911.5614_9.119	rel_abund_log2	b1	b3	12	12	3.417794	11	0.0057500	**
911.7203_7.674	rel_abund_log2	b1	b3	12	12	-2.700105	11	0.0207000	*
911.7206_7.958	rel_abund_log2	b1	b3	12	12	-2.418093	11	0.0341000	*
912.8727_11.58	rel_abund_log2	b1	b3	12	12	3.012375	11	0.0118000	*
913.7001_6.872	rel_abund_log2	b1	b3	12	12	-2.430193	11	0.0334000	*
918.754_11.178	rel_abund_log2	b1	b3	12	12	-2.429060	11	0.0335000	*
919.7465_8.87	rel_abund_log2	b1	b3	12	12	2.830942	11	0.0163000	*
922.7862_11.386	rel_abund_log2	b1	b3	12	12	-3.172938	11	0.0088700	**
924.8016_11.511	rel_abund_log2	b1	b3	12	12	-6.292248	11	0.0000590	****
926.8159_11.647	rel_abund_log2	b1	b3	12	12	-2.973241	11	0.0127000	*
927.7399_11.374	rel_abund_log2	b1	b3	12	12	-2.567552	11	0.0262000	*
928.7831_8.964	rel_abund_log2	b1	b3	12	12	-3.391864	11	0.0060200	**
931.7715_11.646	rel_abund_log2	b1	b3	12	12	-4.320578	11	0.0012100	**
933.7875_11.787	rel_abund_log2	b1	b3	12	12	-2.311993	11	0.0412000	*
936.8015_11.483	rel_abund_log2	b1	b3	12	12	-2.615848	11	0.0240000	*
937.6995_6.371	rel_abund_log2	b1	b3	12	12	-2.953401	11	0.0131000	*
938.8159_11.193	rel_abund_log2	b1	b3	12	12	-3.231213	11	0.0080000	**
941.7308_7.633	rel_abund_log2	b1	b3	12	12	-2.592930	11	0.0250000	*
941.7313_7.957	rel_abund_log2	b1	b3	12	12	-2.824960	11	0.0165000	*
942.7986_8.961	rel_abund_log2	b1	b3	12	12	-2.279060	11	0.0436000	*
950.8156_11.512	rel_abund_log2	b1	b3	12	12	-3.010495	11	0.0119000	*
951.7153_7.77	rel_abund_log2	b1	b3	12	12	-2.600807	11	0.0247000	*
952.831_11.66	rel_abund_log2	b1	b3	12	12	-3.110511	11	0.0099200	**
957.7865_11.655	rel_abund_log2	b1	b3	12	12	-3.166048	11	0.0089800	**
972.8_11.345	rel_abund_log2	b1	b3	12	12	-2.247276	11	0.0461000	*
978.8456_11.66	rel_abund_log2	b1	b3	12	12	-3.276276	11	0.0073800	**
993.7984_10.238	rel_abund_log2	b1	b3	12	12	-2.862384	11	0.0155000	*

# how many are significant?
nrow(red_t.test_paired_sig)

## [1] 452

Remove BG diet

# let's grab both datasets for features sig in pre v post red and pre v post control and combine them
fulljoin_t.test_redANDctrl <- full_join(red_t.test_paired_sig, ctrl_t.test_paired_sig,
                                        by = "mz_rt",
                                        suffix = c(".red", ".ctrl"))

# number of features in combined list
nrow(fulljoin_t.test_redANDctrl)

## [1] 632

Let’s try and remove features significant due to the background diet

sig_paired_red_rmBG <- fulljoin_t.test_redANDctrl %>%
  # add a column to account for direction
  mutate(sign = statistic.red * statistic.ctrl) %>%
  # replace NAs in the sign column with 0
  mutate(sign = replace_na(sign, 0)) %>%
  # replace NAs in the statistic.ctrl column to 0
  mutate(statistic.ctrl = replace_na(statistic.ctrl, 0)) %>%
  # filter for columns that are either negative (means ctrl and tomato are going in opposite dir) or where the stat.ctrl col is 0 (so we don't remove features that are just not present in the sig control list)
  filter((sign < 0 | statistic.ctrl == 0))

# number of features without bg diet effect
nrow(sig_paired_red_rmBG)

## [1] 195

How many BG-diet-related features did we remove from the list of significant beta features?

nrow(red_t.test_paired_sig) - nrow(sig_paired_red_rmBG)

## [1] 257

ANOVA overlap

Keep sig features in t-test that have a match in sig ANOVA. Let’s take our new feature list (background diet effect removed)

# select only features from paired list that have a match in ANOVA list
red_sig_ANOVA_overlap_paired <- inner_join(sig_paired_red_rmBG,
                                            trt_tukeyHSD_sig,
                                            by = "mz_rt",
                                            suffix = c(".t-test", ".tukeys"))

# features overlapping with sig ANOVA
unique(red_sig_ANOVA_overlap_paired$mz_rt)

##  [1] "1526.0908_8.976" "1527.094_8.977"  "1649.3448_9.904" "536.437_10.971" 
##  [5] "605.5497_9.629"  "738.543_8.181"   "748.5258_8.233"  "785.0201_6.879" 
##  [9] "797.6453_8.401"  "813.6866_9.902"  "827.6999_10.389" "865.5795_9.626" 
## [13] "876.5695_7.88"   "878.5852_8.857"  "897.705_7.958"

Paired tomato

# run paired t-tests for control intervention
tomato_t.test_paired <- df_for_stats %>%
  filter(tomato_or_control == "tomato") %>%
 dplyr::select(subject, period, mz_rt, rel_abund_log2) %>%
  group_by(mz_rt) %>%
  t_test(rel_abund_log2 ~ period, 
         paired = TRUE, 
         p.adjust.method = "BH") %>% # Benjamini-Hochberg controlling to lower false positives
  add_significance()

Statistically significant features

# which features are significant?
tomato_t.test_paired_sig <- tomato_t.test_paired %>%
  filter(p < 0.05)
kable(tomato_t.test_paired_sig)

mz_rt	.y.	group1	group2	n1	n2	statistic	df	p	p.signif
1014.9413_11.924	rel_abund_log2	b1	b3	24	24	2.756628	23	0.0112000	*
1017.6873_3.095	rel_abund_log2	b1	b3	24	24	3.816592	23	0.0008860	***
1018.1281_3.082	rel_abund_log2	b1	b3	24	24	-2.446994	23	0.0225000	*
1020.8931_11.508	rel_abund_log2	b1	b3	24	24	-2.464274	23	0.0216000	*
1026.6606_3.098	rel_abund_log2	b1	b3	24	24	2.352023	23	0.0276000	*
1028.9572_12.137	rel_abund_log2	b1	b3	24	24	2.672502	23	0.0136000	*
1034.9093_11.511	rel_abund_log2	b1	b3	24	24	-3.411410	23	0.0023900	**
1036.925_11.654	rel_abund_log2	b1	b3	24	24	-3.556487	23	0.0016800	**
1039.6688_3.106	rel_abund_log2	b1	b3	24	24	4.199101	23	0.0003430	***
1039.6724_2.735	rel_abund_log2	b1	b3	24	24	2.186184	23	0.0392000	*
1041.9353_11.754	rel_abund_log2	b1	b3	24	24	-4.638464	23	0.0001150	***
1061.6537_2.738	rel_abund_log2	b1	b3	24	24	2.110463	23	0.0459000	*
1061.8543_10.506	rel_abund_log2	b1	b3	24	24	3.652203	23	0.0013300	**
1128.671_2.722	rel_abund_log2	b1	b3	24	24	3.024166	23	0.0060400	**
1151.3639_7.711	rel_abund_log2	b1	b3	24	24	4.065182	23	0.0004780	***
1151.8656_7.71	rel_abund_log2	b1	b3	24	24	4.988312	23	0.0000480	****
1155.8333_6.446	rel_abund_log2	b1	b3	24	24	-2.446795	23	0.0225000	*
1157.3434_6.852	rel_abund_log2	b1	b3	24	24	-2.435051	23	0.0231000	*
116.0707_0.623	rel_abund_log2	b1	b3	24	24	3.507298	23	0.0018900	**
1163.3585_7.687	rel_abund_log2	b1	b3	24	24	3.350224	23	0.0027700	**
1169.3422_6.843	rel_abund_log2	b1	b3	24	24	-2.396459	23	0.0251000	*
1171.3299_6.847	rel_abund_log2	b1	b3	24	24	2.072837	23	0.0496000	*
118.0864_0.597	rel_abund_log2	b1	b3	24	24	-7.032818	23	0.0000004	****
1187.3609_7.137	rel_abund_log2	b1	b3	24	24	3.288973	23	0.0032100	**
1187.8641_7.656	rel_abund_log2	b1	b3	24	24	-2.141304	23	0.0431000	*
1187.8642_7.137	rel_abund_log2	b1	b3	24	24	2.435475	23	0.0230000	*
1187.8649_7.285	rel_abund_log2	b1	b3	24	24	-2.362362	23	0.0270000	*
1189.3305_6.84	rel_abund_log2	b1	b3	24	24	-2.366287	23	0.0268000	*
1192.8412_6.709	rel_abund_log2	b1	b3	24	24	-3.482705	23	0.0020100	**
1193.9116_8.867	rel_abund_log2	b1	b3	24	24	3.979780	23	0.0005920	***
1195.8336_6.857	rel_abund_log2	b1	b3	24	24	-2.612341	23	0.0156000	*
1198.856_7.141	rel_abund_log2	b1	b3	24	24	2.658633	23	0.0140000	*
1199.3591_7.285	rel_abund_log2	b1	b3	24	24	-2.454670	23	0.0221000	*
1199.864_7.757	rel_abund_log2	b1	b3	24	24	-2.126008	23	0.0445000	*
1204.4035_8.865	rel_abund_log2	b1	b3	24	24	3.845184	23	0.0008260	***
1204.9038_8.864	rel_abund_log2	b1	b3	24	24	3.664984	23	0.0012900	**
1204.9948_9.897	rel_abund_log2	b1	b3	24	24	3.904721	23	0.0007130	***
1205.4054_8.863	rel_abund_log2	b1	b3	24	24	4.173292	23	0.0003660	***
1205.4975_9.898	rel_abund_log2	b1	b3	24	24	3.811819	23	0.0008970	***
1210.8558_7.304	rel_abund_log2	b1	b3	24	24	2.598275	23	0.0161000	*
1216.4896_9.892	rel_abund_log2	b1	b3	24	24	4.497473	23	0.0001630	***
1216.9911_9.896	rel_abund_log2	b1	b3	24	24	4.691952	23	0.0001000	****
1225.3783_7.964	rel_abund_log2	b1	b3	24	24	2.876339	23	0.0085300	**
1225.88_7.956	rel_abund_log2	b1	b3	24	24	2.146726	23	0.0426000	*
1226.887_7.956	rel_abund_log2	b1	b3	24	24	-2.907790	23	0.0079300	**
1229.9108_8.397	rel_abund_log2	b1	b3	24	24	-3.070067	23	0.0054200	**
1229.9993_9.9	rel_abund_log2	b1	b3	24	24	-2.893940	23	0.0081900	**
1231.5117_9.905	rel_abund_log2	b1	b3	24	24	-4.284925	23	0.0002770	***
1237.8134_10.084	rel_abund_log2	b1	b3	24	24	3.074231	23	0.0053700	**
1241.4043_8.395	rel_abund_log2	b1	b3	24	24	-2.853972	23	0.0089800	**
1242.5051_10.22	rel_abund_log2	b1	b3	24	24	3.673215	23	0.0012600	**
1242.5053_9.906	rel_abund_log2	b1	b3	24	24	-4.582366	23	0.0001320	***
1249.7383_5.838	rel_abund_log2	b1	b3	24	24	-2.157385	23	0.0417000	*
1259.7951_10.087	rel_abund_log2	b1	b3	24	24	2.835315	23	0.0093800	**
1263.8289_10.086	rel_abund_log2	b1	b3	24	24	-2.384164	23	0.0258000	*
1263.8289_10.349	rel_abund_log2	b1	b3	24	24	3.680038	23	0.0012400	**
1265.8449_10.582	rel_abund_log2	b1	b3	24	24	2.276398	23	0.0324000	*
1268.2053_12.079	rel_abund_log2	b1	b3	24	24	-2.249827	23	0.0343000	*
1276.1913_12.079	rel_abund_log2	b1	b3	24	24	-2.103715	23	0.0465000	*
1281.8397_10.519	rel_abund_log2	b1	b3	24	24	3.692553	23	0.0012000	**
1285.8106_10.085	rel_abund_log2	b1	b3	24	24	-3.479606	23	0.0020300	**
1297.1947_10.828	rel_abund_log2	b1	b3	24	24	2.701741	23	0.0127000	*
1298.1748_11.858	rel_abund_log2	b1	b3	24	24	-2.223626	23	0.0363000	*
1298.1983_10.827	rel_abund_log2	b1	b3	24	24	3.290609	23	0.0032000	**
1299.1762_12.078	rel_abund_log2	b1	b3	24	24	-2.075740	23	0.0493000	*
1302.2067_12.075	rel_abund_log2	b1	b3	24	24	2.278947	23	0.0323000	*
1308.157_11.948	rel_abund_log2	b1	b3	24	24	-2.685996	23	0.0132000	*
1316.1245_11.664	rel_abund_log2	b1	b3	24	24	-2.812332	23	0.0098900	**
1318.2126_11.857	rel_abund_log2	b1	b3	24	24	-2.121279	23	0.0449000	*
1319.2346_12.075	rel_abund_log2	b1	b3	24	24	2.568846	23	0.0172000	*
1322.2624_10.832	rel_abund_log2	b1	b3	24	24	3.539935	23	0.0017500	**
1334.1724_11.947	rel_abund_log2	b1	b3	24	24	-2.840458	23	0.0092700	**
1339.0138_6.829	rel_abund_log2	b1	b3	24	24	4.712884	23	0.0000952	****
1339.2017_11.786	rel_abund_log2	b1	b3	24	24	-2.353661	23	0.0275000	*
1341.0272_6.83	rel_abund_log2	b1	b3	24	24	5.709964	23	0.0000082	****
1341.2177_11.895	rel_abund_log2	b1	b3	24	24	-3.988043	23	0.0005790	***
1342.2211_11.896	rel_abund_log2	b1	b3	24	24	-3.070919	23	0.0054100	**
1346.1728_11.908	rel_abund_log2	b1	b3	24	24	-3.523353	23	0.0018200	**
1347.1765_11.909	rel_abund_log2	b1	b3	24	24	-3.074887	23	0.0053600	**
1363.015_6.835	rel_abund_log2	b1	b3	24	24	4.190933	23	0.0003500	***
1364.2059_11.753	rel_abund_log2	b1	b3	24	24	-4.184076	23	0.0003560	***
1365.0287_6.836	rel_abund_log2	b1	b3	24	24	7.106371	23	0.0000003	****
1369.1611_11.766	rel_abund_log2	b1	b3	24	24	-2.460842	23	0.0218000	*
1389.0311_6.813	rel_abund_log2	b1	b3	24	24	5.381553	23	0.0000182	****
1424.1384_8.941	rel_abund_log2	b1	b3	24	24	-2.070168	23	0.0498000	*
1447.1208_6.507	rel_abund_log2	b1	b3	24	24	-2.463114	23	0.0217000	*
1454.1867_9.904	rel_abund_log2	b1	b3	24	24	-5.025228	23	0.0000438	****
1454.1868_9.88	rel_abund_log2	b1	b3	24	24	-5.067277	23	0.0000395	****
1455.1905_9.914	rel_abund_log2	b1	b3	24	24	-4.145057	23	0.0003920	***
1465.1678_7.525	rel_abund_log2	b1	b3	24	24	-2.140800	23	0.0431000	*
1470.0283_7.811	rel_abund_log2	b1	b3	24	24	-3.435923	23	0.0022500	**
1472.1218_6.472	rel_abund_log2	b1	b3	24	24	-2.895900	23	0.0081500	**
1473.1717_7.517	rel_abund_log2	b1	b3	24	24	-3.883038	23	0.0007520	***
1475.1518_7.004	rel_abund_log2	b1	b3	24	24	-5.399438	23	0.0000174	****
1476.1365_7.528	rel_abund_log2	b1	b3	24	24	-4.410957	23	0.0002020	***
1476.1552_7.005	rel_abund_log2	b1	b3	24	24	-4.278709	23	0.0002810	***
1478.1161_7.023	rel_abund_log2	b1	b3	24	24	-2.608798	23	0.0157000	*
1479.1185_7.024	rel_abund_log2	b1	b3	24	24	-2.398538	23	0.0250000	*
1479.137_7.538	rel_abund_log2	b1	b3	24	24	-3.411540	23	0.0023900	**
1481.1402_7.792	rel_abund_log2	b1	b3	24	24	-4.049810	23	0.0004970	***
1487.1502_7.525	rel_abund_log2	b1	b3	24	24	-2.864276	23	0.0087700	**
1487.1511_6.872	rel_abund_log2	b1	b3	24	24	-2.613269	23	0.0155000	*
1487.1515_6.871	rel_abund_log2	b1	b3	24	24	-2.676855	23	0.0135000	*
1488.1552_6.873	rel_abund_log2	b1	b3	24	24	-3.550125	23	0.0017100	**
1497.1674_7.486	rel_abund_log2	b1	b3	24	24	-4.851900	23	0.0000674	****
1499.1861_7.555	rel_abund_log2	b1	b3	24	24	-2.738345	23	0.0117000	*
1500.0993_6.518	rel_abund_log2	b1	b3	24	24	-2.402836	23	0.0247000	*
1500.1349_7.508	rel_abund_log2	b1	b3	24	24	-6.059333	23	0.0000035	****
1502.1505_7.558	rel_abund_log2	b1	b3	24	24	-2.294506	23	0.0312000	*
1503.1775_7.514	rel_abund_log2	b1	b3	24	24	-3.555069	23	0.0016900	**
1504.1822_7.513	rel_abund_log2	b1	b3	24	24	-2.566863	23	0.0172000	*
1506.089_7.132	rel_abund_log2	b1	b3	24	24	2.706950	23	0.0126000	*
1506.1473_7.519	rel_abund_log2	b1	b3	24	24	-2.937593	23	0.0074000	**
1507.0917_7.135	rel_abund_log2	b1	b3	24	24	2.245667	23	0.0346000	*
1507.1178_6.465	rel_abund_log2	b1	b3	24	24	-2.127343	23	0.0443000	*
1508.1468_7.513	rel_abund_log2	b1	b3	24	24	-2.280851	23	0.0321000	*
1508.1474_7.516	rel_abund_log2	b1	b3	24	24	-3.536291	23	0.0017600	**
1509.1328_6.475	rel_abund_log2	b1	b3	24	24	2.973257	23	0.0068000	**
1509.1474_7.507	rel_abund_log2	b1	b3	24	24	-4.106856	23	0.0004310	***
1510.1192_7.742	rel_abund_log2	b1	b3	24	24	2.091928	23	0.0477000	*
1510.1192_7.922	rel_abund_log2	b1	b3	24	24	-2.198173	23	0.0383000	*
1510.1351_6.854	rel_abund_log2	b1	b3	24	24	-2.422800	23	0.0237000	*
1510.1375_6.48	rel_abund_log2	b1	b3	24	24	2.602230	23	0.0159000	*
1510.1552_7.549	rel_abund_log2	b1	b3	24	24	-4.209539	23	0.0003340	***
1511.123_7.922	rel_abund_log2	b1	b3	24	24	-3.277119	23	0.0033100	**
1511.145_6.854	rel_abund_log2	b1	b3	24	24	-3.576163	23	0.0016000	**
1512.1627_7.554	rel_abund_log2	b1	b3	24	24	-3.194264	23	0.0040300	**
1514.1517_7.514	rel_abund_log2	b1	b3	24	24	-4.487575	23	0.0001670	***
1515.1602_7.514	rel_abund_log2	b1	b3	24	24	-2.480254	23	0.0209000	*
1518.2654_8.731	rel_abund_log2	b1	b3	24	24	3.360977	23	0.0027000	**
1520.1651_7.717	rel_abund_log2	b1	b3	24	24	5.422223	23	0.0000165	****
1523.1825_7.536	rel_abund_log2	b1	b3	24	24	-2.710240	23	0.0125000	*
1524.1333_7.404	rel_abund_log2	b1	b3	24	24	-3.821564	23	0.0008750	***
1526.0908_8.976	rel_abund_log2	b1	b3	24	24	-4.080666	23	0.0004600	***
1526.1136_6.481	rel_abund_log2	b1	b3	24	24	-2.540820	23	0.0183000	*
1526.1519_7.532	rel_abund_log2	b1	b3	24	24	-2.772792	23	0.0108000	*
1527.094_8.977	rel_abund_log2	b1	b3	24	24	-4.184747	23	0.0003550	***
1527.1556_7.532	rel_abund_log2	b1	b3	24	24	-2.552367	23	0.0178000	*
1530.1768_7.745	rel_abund_log2	b1	b3	24	24	2.278844	23	0.0323000	*
1531.1683_7.726	rel_abund_log2	b1	b3	24	24	2.165262	23	0.0410000	*
1531.181_7.771	rel_abund_log2	b1	b3	24	24	4.986523	23	0.0000482	****
1531.2124_8.09	rel_abund_log2	b1	b3	24	24	-4.796455	23	0.0000773	****
1531.6527_7.711	rel_abund_log2	b1	b3	24	24	3.751606	23	0.0010400	**
1532.1012_7.882	rel_abund_log2	b1	b3	24	24	-2.416722	23	0.0240000	*
1532.1582_7.713	rel_abund_log2	b1	b3	24	24	5.644479	23	0.0000096	****
1534.154_7.527	rel_abund_log2	b1	b3	24	24	-4.409517	23	0.0002030	***
1534.1768_8.083	rel_abund_log2	b1	b3	24	24	-2.730872	23	0.0119000	*
1535.1521_6.713	rel_abund_log2	b1	b3	24	24	-2.704900	23	0.0126000	*
1535.1558_7.533	rel_abund_log2	b1	b3	24	24	-4.421341	23	0.0001970	***
1536.6252_6.856	rel_abund_log2	b1	b3	24	24	-2.973773	23	0.0068000	**
1537.1733_7.573	rel_abund_log2	b1	b3	24	24	-2.831065	23	0.0094700	**
1538.1135_7.026	rel_abund_log2	b1	b3	24	24	2.609268	23	0.0157000	*
1538.1514_7.472	rel_abund_log2	b1	b3	24	24	-4.926246	23	0.0000560	****
1540.1625_7.542	rel_abund_log2	b1	b3	24	24	-3.046579	23	0.0057300	**
1540.1703_7.761	rel_abund_log2	b1	b3	24	24	-3.709767	23	0.0011500	**
1541.1773_7.535	rel_abund_log2	b1	b3	24	24	-3.532959	23	0.0017800	**
1541.1852_7.515	rel_abund_log2	b1	b3	24	24	-3.334909	23	0.0028800	**
1542.1448_7.703	rel_abund_log2	b1	b3	24	24	4.085352	23	0.0004550	***
1542.1805_8.073	rel_abund_log2	b1	b3	24	24	-2.170637	23	0.0405000	*
1542.2142_6.931	rel_abund_log2	b1	b3	24	24	-2.557678	23	0.0176000	*
1542.645_7.698	rel_abund_log2	b1	b3	24	24	3.478004	23	0.0020300	**
1543.1481_7.702	rel_abund_log2	b1	b3	24	24	4.093289	23	0.0004460	***
1543.2104_7.94	rel_abund_log2	b1	b3	24	24	-4.058835	23	0.0004860	***
1544.1542_7.691	rel_abund_log2	b1	b3	24	24	2.533647	23	0.0186000	*
1544.1549_7.686	rel_abund_log2	b1	b3	24	24	3.211538	23	0.0038700	**
1544.1615_7.508	rel_abund_log2	b1	b3	24	24	-2.584622	23	0.0166000	*
1545.1606_7.669	rel_abund_log2	b1	b3	24	24	2.228903	23	0.0359000	*
1546.1714_7.763	rel_abund_log2	b1	b3	24	24	4.632757	23	0.0001160	***
1546.1749_7.78	rel_abund_log2	b1	b3	24	24	4.877684	23	0.0000632	****
1546.1772_8.02	rel_abund_log2	b1	b3	24	24	3.140138	23	0.0045900	**
1547.1793_7.789	rel_abund_log2	b1	b3	24	24	3.959732	23	0.0006220	***
1547.1803_7.856	rel_abund_log2	b1	b3	24	24	4.958637	23	0.0000517	****
1547.1806_8.02	rel_abund_log2	b1	b3	24	24	3.952200	23	0.0006330	***
1548.0963_7.222	rel_abund_log2	b1	b3	24	24	-2.391455	23	0.0254000	*
1550.1138_6.449	rel_abund_log2	b1	b3	24	24	-3.521698	23	0.0018300	**
1550.1462_7.021	rel_abund_log2	b1	b3	24	24	3.059126	23	0.0055600	**
1550.1483_7.401	rel_abund_log2	b1	b3	24	24	-4.997849	23	0.0000469	****
1554.1833_7.701	rel_abund_log2	b1	b3	24	24	2.829459	23	0.0095000	**
1557.1328_6.71	rel_abund_log2	b1	b3	24	24	-3.139848	23	0.0045900	**
1558.0818_8.319	rel_abund_log2	b1	b3	24	24	-2.471428	23	0.0213000	*
1558.1201_7.783	rel_abund_log2	b1	b3	24	24	-4.103167	23	0.0004350	***
1558.1357_6.713	rel_abund_log2	b1	b3	24	24	-3.797247	23	0.0009300	***
1560.2277_8.857	rel_abund_log2	b1	b3	24	24	2.367304	23	0.0267000	*
1561.1392_7.955	rel_abund_log2	b1	b3	24	24	-3.441087	23	0.0022300	**
1561.2317_8.857	rel_abund_log2	b1	b3	24	24	2.615576	23	0.0155000	*
1562.1488_7.418	rel_abund_log2	b1	b3	24	24	-6.882982	23	0.0000005	****
1562.1493_7.418	rel_abund_log2	b1	b3	24	24	-4.647452	23	0.0001120	***
1563.1166_6.865	rel_abund_log2	b1	b3	24	24	2.570533	23	0.0171000	*
1563.1535_7.418	rel_abund_log2	b1	b3	24	24	-8.727794	23	0.0000000	****
1563.2185_8.966	rel_abund_log2	b1	b3	24	24	-5.915555	23	0.0000050	****
1564.1347_6.888	rel_abund_log2	b1	b3	24	24	-3.034352	23	0.0058900	**
1564.1662_7.508	rel_abund_log2	b1	b3	24	24	-5.217058	23	0.0000273	****
1565.1315_7.095	rel_abund_log2	b1	b3	24	24	2.163874	23	0.0411000	*
1566.1816_7.932	rel_abund_log2	b1	b3	24	24	-3.863905	23	0.0007880	***
1566.2163_6.888	rel_abund_log2	b1	b3	24	24	-2.853777	23	0.0089800	**
1566.6417_7.129	rel_abund_log2	b1	b3	24	24	2.273569	23	0.0326000	*
1567.1909_7.931	rel_abund_log2	b1	b3	24	24	-3.230999	23	0.0036900	**
1569.2427_7.289	rel_abund_log2	b1	b3	24	24	-2.119620	23	0.0450000	*
1572.1304_7.394	rel_abund_log2	b1	b3	24	24	-4.706589	23	0.0000967	****
1574.1454_7.378	rel_abund_log2	b1	b3	24	24	-4.847714	23	0.0000681	****
1574.2638_8.923	rel_abund_log2	b1	b3	24	24	5.074105	23	0.0000388	****
1574.6139_6.862	rel_abund_log2	b1	b3	24	24	2.147372	23	0.0425000	*
1576.1618_7.678	rel_abund_log2	b1	b3	24	24	-2.162281	23	0.0412000	*
1577.1679_7.401	rel_abund_log2	b1	b3	24	24	-2.943901	23	0.0072900	**
1578.1423_7.058	rel_abund_log2	b1	b3	24	24	-2.991713	23	0.0065200	**
1578.1434_7.057	rel_abund_log2	b1	b3	24	24	-3.156550	23	0.0044100	**
1578.178_7.924	rel_abund_log2	b1	b3	24	24	-4.009421	23	0.0005500	***
1579.1825_7.633	rel_abund_log2	b1	b3	24	24	-2.464135	23	0.0216000	*
1579.649_7.139	rel_abund_log2	b1	b3	24	24	3.878030	23	0.0007610	***
1579.6508_7.286	rel_abund_log2	b1	b3	24	24	-2.277585	23	0.0324000	*
1580.1032_7.792	rel_abund_log2	b1	b3	24	24	-5.178222	23	0.0000300	****
1580.1526_7.144	rel_abund_log2	b1	b3	24	24	2.367702	23	0.0267000	*
1580.159_7.285	rel_abund_log2	b1	b3	24	24	-3.354848	23	0.0027400	**
1581.1632_7.283	rel_abund_log2	b1	b3	24	24	-2.908718	23	0.0079100	**
1581.2641_8.744	rel_abund_log2	b1	b3	24	24	-2.229460	23	0.0358000	*
1584.1871_8.061	rel_abund_log2	b1	b3	24	24	2.720274	23	0.0122000	*
1585.1303_6.711	rel_abund_log2	b1	b3	24	24	-2.343413	23	0.0281000	*
1585.2008_8.971	rel_abund_log2	b1	b3	24	24	-8.307335	23	0.0000000	****
1586.1131_6.864	rel_abund_log2	b1	b3	24	24	-3.012202	23	0.0062100	**
1586.1251_6.718	rel_abund_log2	b1	b3	24	24	-2.076696	23	0.0492000	*
1586.2043_8.972	rel_abund_log2	b1	b3	24	24	-7.701546	23	0.0000001	****
1588.1294_6.745	rel_abund_log2	b1	b3	24	24	-2.149570	23	0.0423000	*
1589.1303_6.863	rel_abund_log2	b1	b3	24	24	-2.663860	23	0.0139000	*
1590.1432_6.882	rel_abund_log2	b1	b3	24	24	-2.912075	23	0.0078500	**
1590.143_7.137	rel_abund_log2	b1	b3	24	24	3.131806	23	0.0046800	**
1590.144_7.285	rel_abund_log2	b1	b3	24	24	-2.694116	23	0.0130000	*
1590.1749_7.958	rel_abund_log2	b1	b3	24	24	-3.642208	23	0.0013600	**
1591.1476_7.288	rel_abund_log2	b1	b3	24	24	-2.150481	23	0.0423000	*
1591.1484_6.916	rel_abund_log2	b1	b3	24	24	-3.541557	23	0.0017400	**
1591.1826_7.956	rel_abund_log2	b1	b3	24	24	-5.690026	23	0.0000086	****
1591.2114_7.764	rel_abund_log2	b1	b3	24	24	-3.657099	23	0.0013100	**
1592.1862_7.949	rel_abund_log2	b1	b3	24	24	-2.763217	23	0.0111000	*
1592.5935_6.867	rel_abund_log2	b1	b3	24	24	-3.258309	23	0.0034600	**
1596.1232_6.714	rel_abund_log2	b1	b3	24	24	-2.538043	23	0.0184000	*
1596.195_7.999	rel_abund_log2	b1	b3	24	24	-2.091554	23	0.0477000	*
1596.3093_9.895	rel_abund_log2	b1	b3	24	24	3.929112	23	0.0006710	***
1596.6247_6.715	rel_abund_log2	b1	b3	24	24	-2.522720	23	0.0190000	*
1597.1201_6.716	rel_abund_log2	b1	b3	24	24	-2.716466	23	0.0123000	*
1597.2743_8.003	rel_abund_log2	b1	b3	24	24	-2.498598	23	0.0201000	*
1597.3131_9.894	rel_abund_log2	b1	b3	24	24	4.903024	23	0.0000593	****
1598.1451_7.197	rel_abund_log2	b1	b3	24	24	-4.081485	23	0.0004590	***
160.0606_0.629	rel_abund_log2	b1	b3	24	24	2.280060	23	0.0322000	*
1602.1428_8.354	rel_abund_log2	b1	b3	24	24	-3.064095	23	0.0055000	**
1604.1571_7.283	rel_abund_log2	b1	b3	24	24	-2.110723	23	0.0459000	*
1604.1937_7.739	rel_abund_log2	b1	b3	24	24	-2.840929	23	0.0092600	**
1604.1948_7.675	rel_abund_log2	b1	b3	24	24	-2.818001	23	0.0097600	**
1605.1979_7.674	rel_abund_log2	b1	b3	24	24	-4.717053	23	0.0000942	****
1609.235_8.392	rel_abund_log2	b1	b3	24	24	-3.517983	23	0.0018500	**
1610.1103_6.827	rel_abund_log2	b1	b3	24	24	-3.104523	23	0.0049900	**
1610.32_9.892	rel_abund_log2	b1	b3	24	24	4.745850	23	0.0000877	****
1611.1156_6.852	rel_abund_log2	b1	b3	24	24	-2.487472	23	0.0206000	*
1611.8348_9.901	rel_abund_log2	b1	b3	24	24	2.194757	23	0.0385000	*
1616.16_7.754	rel_abund_log2	b1	b3	24	24	-3.503055	23	0.0019100	**
1617.1626_7.754	rel_abund_log2	b1	b3	24	24	-4.091716	23	0.0004480	***
1619.1782_7.285	rel_abund_log2	b1	b3	24	24	-2.651980	23	0.0142000	*
1619.2973_8.957	rel_abund_log2	b1	b3	24	24	3.002752	23	0.0063500	**
1621.1991_7.959	rel_abund_log2	b1	b3	24	24	-4.726045	23	0.0000921	****
1621.2757_7.96	rel_abund_log2	b1	b3	24	24	-3.764232	23	0.0010100	**
1622.2784_7.959	rel_abund_log2	b1	b3	24	24	-2.933736	23	0.0074600	**
1623.2097_7.994	rel_abund_log2	b1	b3	24	24	-2.723922	23	0.0121000	*
1624.8418_9.901	rel_abund_log2	b1	b3	24	24	-3.025509	23	0.0060200	**
1625.2172_8.052	rel_abund_log2	b1	b3	24	24	-2.449572	23	0.0223000	*
1625.2283_8.396	rel_abund_log2	b1	b3	24	24	-2.094942	23	0.0474000	*
1629.2009_7.725	rel_abund_log2	b1	b3	24	24	-3.490260	23	0.0019700	**
1631.1866_7.959	rel_abund_log2	b1	b3	24	24	-3.710138	23	0.0011500	**
1631.6823_7.958	rel_abund_log2	b1	b3	24	24	-3.223911	23	0.0037600	**
1632.1881_7.956	rel_abund_log2	b1	b3	24	24	-3.243413	23	0.0035900	**
1635.8345_9.901	rel_abund_log2	b1	b3	24	24	-2.611712	23	0.0156000	*
1636.3352_9.901	rel_abund_log2	b1	b3	24	24	-2.388666	23	0.0255000	*
1637.3363_9.913	rel_abund_log2	b1	b3	24	24	-2.667212	23	0.0138000	*
1642.1751_7.953	rel_abund_log2	b1	b3	24	24	-3.249353	23	0.0035400	**
1643.1784_7.953	rel_abund_log2	b1	b3	24	24	-2.403435	23	0.0247000	*
1646.2057_7.997	rel_abund_log2	b1	b3	24	24	-4.233080	23	0.0003150	***
1647.2105_8.001	rel_abund_log2	b1	b3	24	24	-3.601549	23	0.0015000	**
1648.2179_8.411	rel_abund_log2	b1	b3	24	24	-2.277230	23	0.0324000	*
1648.3413_9.902	rel_abund_log2	b1	b3	24	24	-3.915324	23	0.0006940	***
1649.3444_10.225	rel_abund_log2	b1	b3	24	24	5.769806	23	0.0000071	****
1649.3448_9.904	rel_abund_log2	b1	b3	24	24	-4.781220	23	0.0000803	****
1676.3968_11.262	rel_abund_log2	b1	b3	24	24	-2.526704	23	0.0188000	*
1677.4008_11.264	rel_abund_log2	b1	b3	24	24	-2.972988	23	0.0068100	**
1678.4082_11.291	rel_abund_log2	b1	b3	24	24	-2.118481	23	0.0452000	*
1681.1596_8.548	rel_abund_log2	b1	b3	24	24	-2.622537	23	0.0152000	*
188.0707_0.633	rel_abund_log2	b1	b3	24	24	2.603118	23	0.0159000	*
205.1221_0.621	rel_abund_log2	b1	b3	24	24	6.217807	23	0.0000024	****
235.0925_0.646	rel_abund_log2	b1	b3	24	24	-3.720003	23	0.0011200	**
247.1441_0.636	rel_abund_log2	b1	b3	24	24	2.367875	23	0.0267000	*
286.1437_1.13	rel_abund_log2	b1	b3	24	24	-7.290922	23	0.0000002	****
286.2013_0.633	rel_abund_log2	b1	b3	24	24	-2.802545	23	0.0101000	*
293.0984_0.628	rel_abund_log2	b1	b3	24	24	3.331508	23	0.0029000	**
308.1855_0.633	rel_abund_log2	b1	b3	24	24	-16.437607	23	0.0000000	****
310.2012_0.633	rel_abund_log2	b1	b3	24	24	-2.914449	23	0.0078100	**
312.1597_1.413	rel_abund_log2	b1	b3	24	24	-8.957805	23	0.0000000	****
315.0805_0.637	rel_abund_log2	b1	b3	24	24	2.648465	23	0.0144000	*
316.2483_0.679	rel_abund_log2	b1	b3	24	24	2.948590	23	0.0072100	**
316.2608_0.679	rel_abund_log2	b1	b3	24	24	3.047546	23	0.0057100	**
328.2481_0.675	rel_abund_log2	b1	b3	24	24	2.668168	23	0.0137000	*
331.0535_0.634	rel_abund_log2	b1	b3	24	24	4.216703	23	0.0003280	***
332.243_0.64	rel_abund_log2	b1	b3	24	24	2.307170	23	0.0304000	*
337.1048_1.268	rel_abund_log2	b1	b3	24	24	-2.636525	23	0.0147000	*
342.2638_0.69	rel_abund_log2	b1	b3	24	24	2.163273	23	0.0412000	*
344.2792_0.693	rel_abund_log2	b1	b3	24	24	2.778688	23	0.0107000	*
344.2793_0.927	rel_abund_log2	b1	b3	24	24	4.151382	23	0.0003860	***
353.0342_0.629	rel_abund_log2	b1	b3	24	24	2.102531	23	0.0467000	*
370.3549_12.075	rel_abund_log2	b1	b3	24	24	-2.965691	23	0.0069300	**
373.2483_1.147	rel_abund_log2	b1	b3	24	24	-7.031820	23	0.0000004	****
398.3413_0.639	rel_abund_log2	b1	b3	24	24	-2.567322	23	0.0172000	*
426.3575_2.274	rel_abund_log2	b1	b3	24	24	2.997835	23	0.0064200	**
428.3732_2.759	rel_abund_log2	b1	b3	24	24	2.417857	23	0.0239000	*
438.2976_3.525	rel_abund_log2	b1	b3	24	24	-2.120010	23	0.0450000	*
464.1914_0.656	rel_abund_log2	b1	b3	24	24	-6.229234	23	0.0000024	****
466.3289_4.123	rel_abund_log2	b1	b3	24	24	-2.428781	23	0.0234000	*
478.2926_2.855	rel_abund_log2	b1	b3	24	24	2.194570	23	0.0386000	*
480.3082_3.357	rel_abund_log2	b1	b3	24	24	5.578923	23	0.0000112	****
480.3446_3.381	rel_abund_log2	b1	b3	24	24	-2.410227	23	0.0243000	*
482.3238_2.701	rel_abund_log2	b1	b3	24	24	-2.087248	23	0.0481000	*
502.2902_3.355	rel_abund_log2	b1	b3	24	24	4.551643	23	0.0001420	***
510.3551_3.445	rel_abund_log2	b1	b3	24	24	-3.566915	23	0.0016400	**
520.3416_2.737	rel_abund_log2	b1	b3	24	24	2.398232	23	0.0250000	*
521.4196_4.236	rel_abund_log2	b1	b3	24	24	2.826986	23	0.0095600	**
522.3564_3.24	rel_abund_log2	b1	b3	24	24	5.214196	23	0.0000275	****
526.2644_1.714	rel_abund_log2	b1	b3	24	24	-11.468413	23	0.0000000	****
534.2952_3.067	rel_abund_log2	b1	b3	24	24	3.084023	23	0.0052400	**
536.437_11.201	rel_abund_log2	b1	b3	24	24	-3.162134	23	0.0043500	**
538.3859_4.051	rel_abund_log2	b1	b3	24	24	-4.047270	23	0.0005000	***
542.3237_2.234	rel_abund_log2	b1	b3	24	24	2.224065	23	0.0362000	*
543.4017_4.622	rel_abund_log2	b1	b3	24	24	2.657994	23	0.0141000	*
544.3374_3.238	rel_abund_log2	b1	b3	24	24	4.532066	23	0.0001490	***
550.3862_3.859	rel_abund_log2	b1	b3	24	24	3.398101	23	0.0024700	**
552.4018_4.301	rel_abund_log2	b1	b3	24	24	3.766431	23	0.0010000	***
563.4268_4.262	rel_abund_log2	b1	b3	24	24	2.448703	23	0.0224000	*
563.4273_4.415	rel_abund_log2	b1	b3	24	24	4.228972	23	0.0003180	***
577.5185_8.358	rel_abund_log2	b1	b3	24	24	6.183781	23	0.0000026	****
578.3923_2.734	rel_abund_log2	b1	b3	24	24	2.722901	23	0.0121000	*
593.4757_4.54	rel_abund_log2	b1	b3	24	24	7.906852	23	0.0000001	****
593.4762_4.644	rel_abund_log2	b1	b3	24	24	7.988658	23	0.0000000	****
593.4768_4.312	rel_abund_log2	b1	b3	24	24	5.055884	23	0.0000406	****
595.4929_4.645	rel_abund_log2	b1	b3	24	24	9.813731	23	0.0000000	****
596.4154_4.906	rel_abund_log2	b1	b3	24	24	-10.077684	23	0.0000000	****
597.5086_5.242	rel_abund_log2	b1	b3	24	24	6.156187	23	0.0000028	****
605.5497_9.629	rel_abund_log2	b1	b3	24	24	7.859590	23	0.0000001	****
608.4637_5.649	rel_abund_log2	b1	b3	24	24	3.282429	23	0.0032700	**
610.431_5.16	rel_abund_log2	b1	b3	24	24	-5.356891	23	0.0000193	****
610.5401_9.655	rel_abund_log2	b1	b3	24	24	-3.174643	23	0.0042300	**
610.5401_9.784	rel_abund_log2	b1	b3	24	24	-3.502733	23	0.0019200	**
617.4748_4.643	rel_abund_log2	b1	b3	24	24	5.608265	23	0.0000105	****
619.4811_4.462	rel_abund_log2	b1	b3	24	24	2.898692	23	0.0081000	**
619.4903_4.953	rel_abund_log2	b1	b3	24	24	11.942170	23	0.0000000	****
620.5974_10.522	rel_abund_log2	b1	b3	24	24	3.170782	23	0.0042700	**
622.6132_10.675	rel_abund_log2	b1	b3	24	24	3.593438	23	0.0015300	**
623.1355_0.614	rel_abund_log2	b1	b3	24	24	3.290977	23	0.0032000	**
629.1522_0.633	rel_abund_log2	b1	b3	24	24	4.249145	23	0.0003030	***
634.5399_9.061	rel_abund_log2	b1	b3	24	24	-5.462883	23	0.0000149	****
634.54_8.941	rel_abund_log2	b1	b3	24	24	-6.552931	23	0.0000011	****
636.5558_9.845	rel_abund_log2	b1	b3	24	24	-2.504311	23	0.0198000	*
636.5558_9.995	rel_abund_log2	b1	b3	24	24	-2.532135	23	0.0186000	*
642.6107_12.077	rel_abund_log2	b1	b3	24	24	-3.088146	23	0.0051900	**
643.38_0.69	rel_abund_log2	b1	b3	24	24	-4.570304	23	0.0001360	***
645.1222_0.627	rel_abund_log2	b1	b3	24	24	2.588628	23	0.0164000	*
646.6126_10.526	rel_abund_log2	b1	b3	24	24	-2.759588	23	0.0112000	*
650.6449_10.83	rel_abund_log2	b1	b3	24	24	5.110892	23	0.0000354	****
652.6596_10.839	rel_abund_log2	b1	b3	24	24	4.025044	23	0.0005290	***
661.5282_5.288	rel_abund_log2	b1	b3	24	24	-2.873648	23	0.0085800	**
664.6011_11.668	rel_abund_log2	b1	b3	24	24	-2.523831	23	0.0190000	*
673.5905_11.756	rel_abund_log2	b1	b3	24	24	-3.365534	23	0.0026700	**
677.5586_5.973	rel_abund_log2	b1	b3	24	24	-3.010173	23	0.0062400	**
690.6188_11.754	rel_abund_log2	b1	b3	24	24	-4.290100	23	0.0002730	***
691.5742_6.403	rel_abund_log2	b1	b3	24	24	-4.030814	23	0.0005210	***
692.5581_7.191	rel_abund_log2	b1	b3	24	24	-2.417265	23	0.0240000	*
692.6335_11.899	rel_abund_log2	b1	b3	24	24	-4.190548	23	0.0003500	***
693.6368_11.899	rel_abund_log2	b1	b3	24	24	-4.478096	23	0.0001710	***
695.5093_5.094	rel_abund_log2	b1	b3	24	24	-3.337246	23	0.0028600	**
695.5729_11.772	rel_abund_log2	b1	b3	24	24	-3.198892	23	0.0039900	**
700.5274_7.902	rel_abund_log2	b1	b3	24	24	-2.584647	23	0.0166000	*
704.3938_6.501	rel_abund_log2	b1	b3	24	24	2.541497	23	0.0182000	*
715.5745_6.252	rel_abund_log2	b1	b3	24	24	-2.354525	23	0.0275000	*
717.5903_6.828	rel_abund_log2	b1	b3	24	24	-3.245767	23	0.0035700	**
717.5903_7.018	rel_abund_log2	b1	b3	24	24	-5.391203	23	0.0000178	****
718.5742_8.138	rel_abund_log2	b1	b3	24	24	-3.785746	23	0.0009560	***
718.5934_7.015	rel_abund_log2	b1	b3	24	24	-4.179711	23	0.0003600	***
719.6006_7.392	rel_abund_log2	b1	b3	24	24	-5.406821	23	0.0000171	****
720.5532_7.039	rel_abund_log2	b1	b3	24	24	-2.965205	23	0.0069300	**
720.5897_8.289	rel_abund_log2	b1	b3	24	24	-2.533306	23	0.0186000	*
724.5277_7.806	rel_abund_log2	b1	b3	24	24	-3.923327	23	0.0006800	***
726.5428_8.237	rel_abund_log2	b1	b3	24	24	-4.490765	23	0.0001660	***
727.5745_6.067	rel_abund_log2	b1	b3	24	24	-4.036529	23	0.0005140	***
729.5912_6.714	rel_abund_log2	b1	b3	24	24	-3.080356	23	0.0052900	**
731.6047_7.112	rel_abund_log2	b1	b3	24	24	-2.382030	23	0.0259000	*
731.6073_7.511	rel_abund_log2	b1	b3	24	24	-2.165599	23	0.0410000	*
732.589_8.53	rel_abund_log2	b1	b3	24	24	-4.746367	23	0.0000876	****
733.6212_7.929	rel_abund_log2	b1	b3	24	24	-3.413026	23	0.0023800	**
733.6215_7.929	rel_abund_log2	b1	b3	24	24	-3.397073	23	0.0024800	**
738.543_8.181	rel_abund_log2	b1	b3	24	24	-6.051597	23	0.0000036	****
740.5559_8.156	rel_abund_log2	b1	b3	24	24	-3.459299	23	0.0021300	**
740.5626_6.841	rel_abund_log2	b1	b3	24	24	-3.029450	23	0.0059600	**
740.5662_6.833	rel_abund_log2	b1	b3	24	24	-2.878694	23	0.0084800	**
742.5377_7.462	rel_abund_log2	b1	b3	24	24	2.427332	23	0.0234000	*
742.5718_8.28	rel_abund_log2	b1	b3	24	24	-3.450239	23	0.0021800	**
742.5753_7.523	rel_abund_log2	b1	b3	24	24	-4.598655	23	0.0001270	***
743.6054_7.227	rel_abund_log2	b1	b3	24	24	-6.536167	23	0.0000011	****
744.5538_6.51	rel_abund_log2	b1	b3	24	24	-2.595662	23	0.0162000	*
745.6202_7.852	rel_abund_log2	b1	b3	24	24	-5.066454	23	0.0000396	****
745.6214_8.115	rel_abund_log2	b1	b3	24	24	-4.271424	23	0.0002860	***
746.5091_7.814	rel_abund_log2	b1	b3	24	24	-4.223404	23	0.0003230	***
746.5685_9.239	rel_abund_log2	b1	b3	24	24	4.042711	23	0.0005060	***
746.6055_8.435	rel_abund_log2	b1	b3	24	24	2.340698	23	0.0283000	*
748.5258_8.233	rel_abund_log2	b1	b3	24	24	-4.359582	23	0.0002300	***
748.5844_7.89	rel_abund_log2	b1	b3	24	24	-2.401847	23	0.0248000	*
748.5844_8.099	rel_abund_log2	b1	b3	24	24	-2.680982	23	0.0133000	*
749.5557_6.066	rel_abund_log2	b1	b3	24	24	-4.229408	23	0.0003180	***
751.5717_6.812	rel_abund_log2	b1	b3	24	24	-2.256005	23	0.0339000	*
752.5592_8.969	rel_abund_log2	b1	b3	24	24	-3.382002	23	0.0025700	**
752.5594_8.973	rel_abund_log2	b1	b3	24	24	-4.160124	23	0.0003780	***
752.574_6.803	rel_abund_log2	b1	b3	24	24	-2.290898	23	0.0315000	*
754.5356_6.825	rel_abund_log2	b1	b3	24	24	2.336753	23	0.0285000	*
754.5373_6.002	rel_abund_log2	b1	b3	24	24	-2.240912	23	0.0350000	*
755.6054_6.838	rel_abund_log2	b1	b3	24	24	-5.047160	23	0.0000415	****
756.5542_6.373	rel_abund_log2	b1	b3	24	24	-2.143416	23	0.0429000	*
756.5904_7.889	rel_abund_log2	b1	b3	24	24	-3.993224	23	0.0005720	***
759.0903_6.983	rel_abund_log2	b1	b3	24	24	2.230457	23	0.0358000	*
759.1221_6.983	rel_abund_log2	b1	b3	24	24	2.071162	23	0.0497000	*
759.6382_8.714	rel_abund_log2	b1	b3	24	24	2.984600	23	0.0066300	**
760.421_6.983	rel_abund_log2	b1	b3	24	24	2.208938	23	0.0374000	*
760.5281_6.984	rel_abund_log2	b1	b3	24	24	2.323924	23	0.0293000	*
760.5329_6.983	rel_abund_log2	b1	b3	24	24	2.199625	23	0.0382000	*
760.5459_7.719	rel_abund_log2	b1	b3	24	24	2.731704	23	0.0119000	*
760.5883_7.719	rel_abund_log2	b1	b3	24	24	6.570374	23	0.0000010	****
760.9763_7.718	rel_abund_log2	b1	b3	24	24	3.014294	23	0.0061800	**
761.1927_7.717	rel_abund_log2	b1	b3	24	24	4.061529	23	0.0004830	***
761.2595_7.716	rel_abund_log2	b1	b3	24	24	4.448148	23	0.0001840	***
761.302_7.716	rel_abund_log2	b1	b3	24	24	5.333808	23	0.0000205	****
761.3174_7.717	rel_abund_log2	b1	b3	24	24	4.594537	23	0.0001280	***
761.329_7.717	rel_abund_log2	b1	b3	24	24	4.360148	23	0.0002290	***
761.3681_7.718	rel_abund_log2	b1	b3	24	24	5.382642	23	0.0000181	****
761.3781_7.717	rel_abund_log2	b1	b3	24	24	4.262833	23	0.0002920	***
761.4062_7.717	rel_abund_log2	b1	b3	24	24	4.155622	23	0.0003820	***
761.4141_7.718	rel_abund_log2	b1	b3	24	24	4.068197	23	0.0004750	***
761.4437_7.717	rel_abund_log2	b1	b3	24	24	3.847667	23	0.0008210	***
761.4545_7.719	rel_abund_log2	b1	b3	24	24	5.045915	23	0.0000416	****
761.5191_7.716	rel_abund_log2	b1	b3	24	24	5.181053	23	0.0000298	****
761.6526_9.142	rel_abund_log2	b1	b3	24	24	-2.572100	23	0.0170000	*
762.5032_7.719	rel_abund_log2	b1	b3	24	24	5.594865	23	0.0000108	****
762.5251_7.716	rel_abund_log2	b1	b3	24	24	4.103061	23	0.0004350	***
762.5433_7.717	rel_abund_log2	b1	b3	24	24	4.808430	23	0.0000751	****
762.6465_8.683	rel_abund_log2	b1	b3	24	24	2.432602	23	0.0232000	*
764.5195_7.458	rel_abund_log2	b1	b3	24	24	3.959266	23	0.0006220	***
764.5566_7.52	rel_abund_log2	b1	b3	24	24	-2.584565	23	0.0166000	*
766.5713_8.323	rel_abund_log2	b1	b3	24	24	3.619641	23	0.0014400	**
766.5755_7.417	rel_abund_log2	b1	b3	24	24	-7.049141	23	0.0000004	****
768.5536_6.434	rel_abund_log2	b1	b3	24	24	-4.786474	23	0.0000793	****
768.5859_8.446	rel_abund_log2	b1	b3	24	24	2.669412	23	0.0137000	*
768.587_8.451	rel_abund_log2	b1	b3	24	24	4.336779	23	0.0002430	***
768.5909_7.579	rel_abund_log2	b1	b3	24	24	-2.080306	23	0.0488000	*
770.0615_7.009	rel_abund_log2	b1	b3	24	24	2.098340	23	0.0471000	*
770.5691_6.825	rel_abund_log2	b1	b3	24	24	-3.361826	23	0.0027000	**
770.6056_8.664	rel_abund_log2	b1	b3	24	24	-2.680076	23	0.0134000	*
771.5762_7.712	rel_abund_log2	b1	b3	24	24	4.527700	23	0.0001510	***
771.6366_8.357	rel_abund_log2	b1	b3	24	24	-3.325112	23	0.0029500	**
772.5245_7.894	rel_abund_log2	b1	b3	24	24	-2.574640	23	0.0169000	*
772.5855_7.501	rel_abund_log2	b1	b3	24	24	-2.724478	23	0.0121000	*
772.6196_8.643	rel_abund_log2	b1	b3	24	24	3.422807	23	0.0023300	**
773.6516_9.025	rel_abund_log2	b1	b3	24	24	-6.643316	23	0.0000009	****
774.5401_8.626	rel_abund_log2	b1	b3	24	24	-2.205703	23	0.0377000	*
774.5404_8.982	rel_abund_log2	b1	b3	24	24	-3.863682	23	0.0007890	***
775.6384_6.825	rel_abund_log2	b1	b3	24	24	3.151765	23	0.0044600	**
775.6661_9.814	rel_abund_log2	b1	b3	24	24	-4.487832	23	0.0001670	***
776.579_6.552	rel_abund_log2	b1	b3	24	24	2.408966	23	0.0244000	*
778.5355_6.376	rel_abund_log2	b1	b3	24	24	-2.088578	23	0.0480000	*
778.5376_5.842	rel_abund_log2	b1	b3	24	24	-2.703411	23	0.0127000	*
780.5519_6.84	rel_abund_log2	b1	b3	24	24	3.042888	23	0.0057800	**
780.5921_7.79	rel_abund_log2	b1	b3	24	24	-6.265133	23	0.0000022	****
781.6192_8.718	rel_abund_log2	b1	b3	24	24	4.235728	23	0.0003130	***
781.62_7.043	rel_abund_log2	b1	b3	24	24	-6.462174	23	0.0000014	****
783.2313_6.877	rel_abund_log2	b1	b3	24	24	-2.424275	23	0.0236000	*
783.332_6.875	rel_abund_log2	b1	b3	24	24	-2.224280	23	0.0362000	*
783.3522_6.877	rel_abund_log2	b1	b3	24	24	-2.317622	23	0.0297000	*
783.4174_6.877	rel_abund_log2	b1	b3	24	24	-2.332307	23	0.0288000	*
783.5024_6.875	rel_abund_log2	b1	b3	24	24	-2.393950	23	0.0252000	*
783.6251_8.708	rel_abund_log2	b1	b3	24	24	2.420702	23	0.0238000	*
783.6368_7.847	rel_abund_log2	b1	b3	24	24	-6.635750	23	0.0000009	****
784.5879_7.141	rel_abund_log2	b1	b3	24	24	3.588251	23	0.0015500	**
784.6308_6.874	rel_abund_log2	b1	b3	24	24	-2.276756	23	0.0324000	*
784.6654_10.38	rel_abund_log2	b1	b3	24	24	5.641168	23	0.0000097	****
784.779_6.877	rel_abund_log2	b1	b3	24	24	-3.094526	23	0.0051100	**
784.7847_6.875	rel_abund_log2	b1	b3	24	24	-2.543744	23	0.0181000	*
785.0064_6.878	rel_abund_log2	b1	b3	24	24	-2.729866	23	0.0119000	*
785.0201_6.879	rel_abund_log2	b1	b3	24	24	-3.162337	23	0.0043500	**
785.0389_6.88	rel_abund_log2	b1	b3	24	24	-3.229424	23	0.0037100	**
785.452_7.291	rel_abund_log2	b1	b3	24	24	-2.114521	23	0.0455000	*
785.5792_6.874	rel_abund_log2	b1	b3	24	24	-2.996845	23	0.0064400	**
785.6536_8.747	rel_abund_log2	b1	b3	24	24	-4.369686	23	0.0002240	***
785.6542_8.953	rel_abund_log2	b1	b3	24	24	4.422767	23	0.0001960	***
786.5814_6.876	rel_abund_log2	b1	b3	24	24	-2.989887	23	0.0065400	**
786.6508_7.289	rel_abund_log2	b1	b3	24	24	-2.150284	23	0.0423000	*
787.6674_9.202	rel_abund_log2	b1	b3	24	24	-7.596889	23	0.0000001	****
787.6701_9.896	rel_abund_log2	b1	b3	24	24	4.203944	23	0.0003390	***
788.5563_7.415	rel_abund_log2	b1	b3	24	24	-6.322305	23	0.0000019	****
788.6177_8.867	rel_abund_log2	b1	b3	24	24	3.585064	23	0.0015700	**
789.6837_10.343	rel_abund_log2	b1	b3	24	24	-2.421797	23	0.0237000	*
790.5352_6.424	rel_abund_log2	b1	b3	24	24	-2.996956	23	0.0064400	**
790.5744_7.193	rel_abund_log2	b1	b3	24	24	-4.718453	23	0.0000939	****
791.5397_6.411	rel_abund_log2	b1	b3	24	24	-2.481984	23	0.0208000	*
791.5433_6.743	rel_abund_log2	b1	b3	24	24	2.075631	23	0.0493000	*
792.5512_6.827	rel_abund_log2	b1	b3	24	24	-2.311696	23	0.0301000	*
792.5531_6.261	rel_abund_log2	b1	b3	24	24	-3.837824	23	0.0008410	***
792.5873_8.665	rel_abund_log2	b1	b3	24	24	-2.389761	23	0.0254000	*
792.5893_7.058	rel_abund_log2	b1	b3	24	24	3.618491	23	0.0014400	**
792.5899_7.556	rel_abund_log2	b1	b3	24	24	-3.410582	23	0.0024000	**
793.556_6.829	rel_abund_log2	b1	b3	24	24	-2.157729	23	0.0416000	*
794.5862_7.544	rel_abund_log2	b1	b3	24	24	-3.761681	23	0.0010100	**
794.6054_8.214	rel_abund_log2	b1	b3	24	24	-4.139427	23	0.0003980	***
794.6057_8.544	rel_abund_log2	b1	b3	24	24	-3.307904	23	0.0030700	**
794.6868_10.627	rel_abund_log2	b1	b3	24	24	2.338175	23	0.0284000	*
796.5222_9.007	rel_abund_log2	b1	b3	24	24	-3.369938	23	0.0026400	**
796.5247_6.843	rel_abund_log2	b1	b3	24	24	2.690673	23	0.0131000	*
796.5845_7.27	rel_abund_log2	b1	b3	24	24	-4.361776	23	0.0002290	***
796.5848_7.423	rel_abund_log2	b1	b3	24	24	-6.673861	23	0.0000008	****
796.621_8.383	rel_abund_log2	b1	b3	24	24	-3.318646	23	0.0029900	**
797.6383_8.603	rel_abund_log2	b1	b3	24	24	-3.691394	23	0.0012100	**
797.6453_8.401	rel_abund_log2	b1	b3	24	24	-5.685006	23	0.0000087	****
798.6003_7.83	rel_abund_log2	b1	b3	24	24	-5.036149	23	0.0000426	****
798.6518_8.391	rel_abund_log2	b1	b3	24	24	-7.067732	23	0.0000003	****
798.681_10.534	rel_abund_log2	b1	b3	24	24	-2.671838	23	0.0136000	*
799.6069_8.865	rel_abund_log2	b1	b3	24	24	3.592181	23	0.0015400	**
799.6686_9.32	rel_abund_log2	b1	b3	24	24	-5.905750	23	0.0000051	****
800.5194_5.843	rel_abund_log2	b1	b3	24	24	-2.546655	23	0.0180000	*
800.5726_6.827	rel_abund_log2	b1	b3	24	24	2.221972	23	0.0364000	*
800.6163_8.637	rel_abund_log2	b1	b3	24	24	-2.574850	23	0.0169000	*
800.616_8.426	rel_abund_log2	b1	b3	24	24	-2.331423	23	0.0289000	*
802.5716_10.204	rel_abund_log2	b1	b3	24	24	-2.253884	23	0.0340000	*
802.5717_7.81	rel_abund_log2	b1	b3	24	24	-4.351264	23	0.0002350	***
804.5876_8.154	rel_abund_log2	b1	b3	24	24	-2.460781	23	0.0218000	*
804.5937_7.544	rel_abund_log2	b1	b3	24	24	-3.908953	23	0.0007050	***
805.0108_3.238	rel_abund_log2	b1	b3	24	24	4.366316	23	0.0002260	***
805.6187_7.848	rel_abund_log2	b1	b3	24	24	-5.468388	23	0.0000147	****
805.6784_9.291	rel_abund_log2	b1	b3	24	24	2.527828	23	0.0188000	*
806.5698_6.37	rel_abund_log2	b1	b3	24	24	-4.111588	23	0.0004260	***
806.6235_6.835	rel_abund_log2	b1	b3	24	24	-2.233371	23	0.0355000	*
807.6349_8.948	rel_abund_log2	b1	b3	24	24	7.137075	23	0.0000003	****
807.6359_7.483	rel_abund_log2	b1	b3	24	24	-4.981387	23	0.0000488	****
808.6375_8.751	rel_abund_log2	b1	b3	24	24	-5.844611	23	0.0000059	****
809.6507_9.876	rel_abund_log2	b1	b3	24	24	5.609443	23	0.0000104	****
809.6525_8.065	rel_abund_log2	b1	b3	24	24	-6.163797	23	0.0000027	****
810.5988_8.865	rel_abund_log2	b1	b3	24	24	3.838724	23	0.0008390	***
810.6013_7.646	rel_abund_log2	b1	b3	24	24	-3.961716	23	0.0006190	***
810.6041_7.959	rel_abund_log2	b1	b3	24	24	-4.014022	23	0.0005430	***
810.6564_8.03	rel_abund_log2	b1	b3	24	24	-5.706567	23	0.0000082	****
810.6812_10.387	rel_abund_log2	b1	b3	24	24	-3.748138	23	0.0010500	**
811.0052_7.959	rel_abund_log2	b1	b3	24	24	-3.522376	23	0.0018300	**
811.2977_7.959	rel_abund_log2	b1	b3	24	24	-4.502504	23	0.0001610	***
811.3445_7.959	rel_abund_log2	b1	b3	24	24	-2.394706	23	0.0252000	*
811.4149_7.961	rel_abund_log2	b1	b3	24	24	-3.195496	23	0.0040200	**
811.4537_7.958	rel_abund_log2	b1	b3	24	24	-3.419826	23	0.0023400	**
811.5374_7.96	rel_abund_log2	b1	b3	24	24	-2.573407	23	0.0170000	*
811.6292_7.819	rel_abund_log2	b1	b3	24	24	-5.538839	23	0.0000124	****
811.6598_8.009	rel_abund_log2	b1	b3	24	24	-2.305849	23	0.0305000	*
811.6703_8.961	rel_abund_log2	b1	b3	24	24	-4.376785	23	0.0002200	***
812.5189_7.959	rel_abund_log2	b1	b3	24	24	-2.308736	23	0.0303000	*
812.545_7.958	rel_abund_log2	b1	b3	24	24	-2.883684	23	0.0083800	**
812.5561_7.198	rel_abund_log2	b1	b3	24	24	-4.275169	23	0.0002840	***
812.615_8.136	rel_abund_log2	b1	b3	24	24	3.029457	23	0.0059600	**
812.6186_8.396	rel_abund_log2	b1	b3	24	24	-2.112807	23	0.0457000	*
812.6971_10.626	rel_abund_log2	b1	b3	24	24	2.168623	23	0.0407000	*
813.6106_7.959	rel_abund_log2	b1	b3	24	24	-3.531679	23	0.0017800	**
813.6834_9.437	rel_abund_log2	b1	b3	24	24	-8.493589	23	0.0000000	****
813.6854_10.226	rel_abund_log2	b1	b3	24	24	4.436593	23	0.0001900	***
813.6866_9.902	rel_abund_log2	b1	b3	24	24	-4.074265	23	0.0004680	***
814.5355_8.122	rel_abund_log2	b1	b3	24	24	-2.265620	23	0.0332000	*
814.535_6.26	rel_abund_log2	b1	b3	24	24	-2.894771	23	0.0081700	**
814.6127_7.96	rel_abund_log2	b1	b3	24	24	-3.002326	23	0.0063600	**
814.6318_9.087	rel_abund_log2	b1	b3	24	24	-2.180895	23	0.0397000	*
815.5406_6.313	rel_abund_log2	b1	b3	24	24	-2.238198	23	0.0352000	*
815.6606_9.084	rel_abund_log2	b1	b3	24	24	-2.572207	23	0.0170000	*
815.7025_10.568	rel_abund_log2	b1	b3	24	24	4.832854	23	0.0000706	****
816.5872_8.538	rel_abund_log2	b1	b3	24	24	-3.415475	23	0.0023700	**
816.5873_8.216	rel_abund_log2	b1	b3	24	24	-3.460704	23	0.0021200	**
816.7069_11.125	rel_abund_log2	b1	b3	24	24	-2.199425	23	0.0382000	*
817.7051_10.344	rel_abund_log2	b1	b3	24	24	-5.923167	23	0.0000049	****
818.0604_6.711	rel_abund_log2	b1	b3	24	24	-2.271678	23	0.0328000	*
818.5668_7.42	rel_abund_log2	b1	b3	24	24	-4.392132	23	0.0002120	***
818.6044_8.299	rel_abund_log2	b1	b3	24	24	-2.688770	23	0.0131000	*
818.6049_7.793	rel_abund_log2	b1	b3	24	24	-2.620256	23	0.0153000	*
819.5737_7.769	rel_abund_log2	b1	b3	24	24	-2.128848	23	0.0442000	*
820.5823_7.852	rel_abund_log2	b1	b3	24	24	-2.749020	23	0.0114000	*
820.5846_7.213	rel_abund_log2	b1	b3	24	24	-5.247673	23	0.0000253	****
820.6202_7.907	rel_abund_log2	b1	b3	24	24	-3.816585	23	0.0008860	***
820.6961_11.574	rel_abund_log2	b1	b3	24	24	-6.238175	23	0.0000023	****
822.0921_7.956	rel_abund_log2	b1	b3	24	24	-2.212681	23	0.0371000	*
822.5404_7.148	rel_abund_log2	b1	b3	24	24	2.178981	23	0.0398000	*
822.5994_7.509	rel_abund_log2	b1	b3	24	24	-5.561911	23	0.0000117	****
823.6064_8.398	rel_abund_log2	b1	b3	24	24	-2.592032	23	0.0163000	*
824.616_8.544	rel_abund_log2	b1	b3	24	24	-4.304151	23	0.0002640	***
824.6527_9.167	rel_abund_log2	b1	b3	24	24	-2.284927	23	0.0319000	*
825.6241_9.886	rel_abund_log2	b1	b3	24	24	3.819372	23	0.0008800	***
825.6473_7.81	rel_abund_log2	b1	b3	24	24	-3.890188	23	0.0007390	***
825.6778_9.575	rel_abund_log2	b1	b3	24	24	-3.362008	23	0.0027000	**
825.6815_9.335	rel_abund_log2	b1	b3	24	24	-12.432634	23	0.0000000	****
825.682_9.561	rel_abund_log2	b1	b3	24	24	-5.125960	23	0.0000341	****
826.5348_5.993	rel_abund_log2	b1	b3	24	24	-2.489700	23	0.0205000	*
826.5716_8.868	rel_abund_log2	b1	b3	24	24	3.005861	23	0.0063000	**
827.663_8.483	rel_abund_log2	b1	b3	24	24	-6.887401	23	0.0000005	****
827.6998_10.277	rel_abund_log2	b1	b3	24	24	-8.598528	23	0.0000000	****
827.6999_10.389	rel_abund_log2	b1	b3	24	24	-5.648085	23	0.0000095	****
827.6999_10.502	rel_abund_log2	b1	b3	24	24	-4.531562	23	0.0001500	***
828.5511_6.373	rel_abund_log2	b1	b3	24	24	-4.196726	23	0.0003450	***
828.6106_7.766	rel_abund_log2	b1	b3	24	24	-3.253698	23	0.0035000	**
828.702_10.497	rel_abund_log2	b1	b3	24	24	-3.880464	23	0.0007570	***
829.6782_8.401	rel_abund_log2	b1	b3	24	24	-5.597191	23	0.0000107	****
829.6788_9.393	rel_abund_log2	b1	b3	24	24	-2.082631	23	0.0486000	*
829.7155_10.657	rel_abund_log2	b1	b3	24	24	-2.961821	23	0.0069900	**
830.568_6.228	rel_abund_log2	b1	b3	24	24	-2.506941	23	0.0197000	*
830.6471_9.551	rel_abund_log2	b1	b3	24	24	-3.641332	23	0.0013600	**
831.6353_8.196	rel_abund_log2	b1	b3	24	24	-7.895269	23	0.0000001	****
831.694_9.318	rel_abund_log2	b1	b3	24	24	-3.036217	23	0.0058700	**
832.5817_7.451	rel_abund_log2	b1	b3	24	24	2.726347	23	0.0120000	*
832.582_7.631	rel_abund_log2	b1	b3	24	24	-6.173864	23	0.0000027	****
833.6507_8.971	rel_abund_log2	b1	b3	24	24	-3.321200	23	0.0029700	**
834.5987_8.394	rel_abund_log2	b1	b3	24	24	-3.513913	23	0.0018600	**
834.6002_7.257	rel_abund_log2	b1	b3	24	24	-6.625355	23	0.0000009	****
834.602_7.751	rel_abund_log2	b1	b3	24	24	-3.454234	23	0.0021600	**
835.5327_7.555	rel_abund_log2	b1	b3	24	24	-2.163889	23	0.0411000	*
835.6566_8.968	rel_abund_log2	b1	b3	24	24	-2.400271	23	0.0249000	*
835.6663_8.407	rel_abund_log2	b1	b3	24	24	-4.447117	23	0.0001850	***
835.6664_10.22	rel_abund_log2	b1	b3	24	24	5.234571	23	0.0000261	****
835.6666_9.91	rel_abund_log2	b1	b3	24	24	-5.434559	23	0.0000160	****
836.6136_9.086	rel_abund_log2	b1	b3	24	24	-2.358852	23	0.0272000	*
836.6148_8.439	rel_abund_log2	b1	b3	24	24	-3.124144	23	0.0047700	**
836.6167_8.053	rel_abund_log2	b1	b3	24	24	-2.099029	23	0.0470000	*
837.5477_8.363	rel_abund_log2	b1	b3	24	24	7.192337	23	0.0000003	****
837.614_7.764	rel_abund_log2	b1	b3	24	24	-3.419908	23	0.0023400	**
837.6724_9.906	rel_abund_log2	b1	b3	24	24	-2.650800	23	0.0143000	*
837.6789_10.324	rel_abund_log2	b1	b3	24	24	-3.803712	23	0.0009150	***
837.6818_10.567	rel_abund_log2	b1	b3	24	24	2.797581	23	0.0102000	*
837.6831_9.217	rel_abund_log2	b1	b3	24	24	-3.730008	23	0.0011000	**
838.6301_9.147	rel_abund_log2	b1	b3	24	24	2.610578	23	0.0156000	*
838.6321_8.779	rel_abund_log2	b1	b3	24	24	-3.243784	23	0.0035800	**
839.6989_10.178	rel_abund_log2	b1	b3	24	24	-4.180361	23	0.0003590	***
840.5868_7.472	rel_abund_log2	b1	b3	24	24	2.394797	23	0.0252000	*
842.5663_7.213	rel_abund_log2	b1	b3	24	24	-3.461926	23	0.0021200	**
842.723_11.155	rel_abund_log2	b1	b3	24	24	-3.988176	23	0.0005790	***
844.7397_11.265	rel_abund_log2	b1	b3	24	24	-3.789557	23	0.0009470	***
846.6641_8.878	rel_abund_log2	b1	b3	24	24	3.066952	23	0.0054600	**
847.6893_7.716	rel_abund_log2	b1	b3	24	24	4.436797	23	0.0001900	***
848.6521_9.028	rel_abund_log2	b1	b3	24	24	-7.790473	23	0.0000001	****
849.6682_6.746	rel_abund_log2	b1	b3	24	24	2.947365	23	0.0072300	**
849.6939_11.262	rel_abund_log2	b1	b3	24	24	-3.006961	23	0.0062900	**
851.64_9.902	rel_abund_log2	b1	b3	24	24	-3.694864	23	0.0012000	**
852.5508_6.228	rel_abund_log2	b1	b3	24	24	-2.348131	23	0.0278000	*
852.6843_10.599	rel_abund_log2	b1	b3	24	24	2.423696	23	0.0236000	*
853.6786_8.974	rel_abund_log2	b1	b3	24	24	-3.907178	23	0.0007080	***
853.6786_8.975	rel_abund_log2	b1	b3	24	24	-3.441902	23	0.0022200	**
854.574_8.362	rel_abund_log2	b1	b3	24	24	5.755834	23	0.0000073	****
856.5819_7.24	rel_abund_log2	b1	b3	24	24	-5.296024	23	0.0000225	****
856.5823_7.778	rel_abund_log2	b1	b3	24	24	-4.480786	23	0.0001700	***
858.5914_7.792	rel_abund_log2	b1	b3	24	24	-4.482889	23	0.0001690	***
858.5977_8.453	rel_abund_log2	b1	b3	24	24	-3.839074	23	0.0008380	***
858.5992_7.06	rel_abund_log2	b1	b3	24	24	-4.422044	23	0.0001970	***
858.7545_11.345	rel_abund_log2	b1	b3	24	24	-4.964898	23	0.0000509	****
859.53_8.35	rel_abund_log2	b1	b3	24	24	8.289635	23	0.0000000	****
859.6891_7.519	rel_abund_log2	b1	b3	24	24	-4.048987	23	0.0004980	***
860.6131_9.152	rel_abund_log2	b1	b3	24	24	2.187421	23	0.0391000	*
860.6133_8.785	rel_abund_log2	b1	b3	24	24	-4.395073	23	0.0002100	***
860.7704_11.477	rel_abund_log2	b1	b3	24	24	-2.620741	23	0.0153000	*
861.7048_7.713	rel_abund_log2	b1	b3	24	24	4.279419	23	0.0002810	***
862.6306_8.44	rel_abund_log2	b1	b3	24	24	-2.115622	23	0.0454000	*
863.6838_8.874	rel_abund_log2	b1	b3	24	24	3.207465	23	0.0039100	**
863.7092_11.343	rel_abund_log2	b1	b3	24	24	-3.570092	23	0.0016200	**
863.7199_8.448	rel_abund_log2	b1	b3	24	24	2.201969	23	0.0380000	*
865.5795_9.626	rel_abund_log2	b1	b3	24	24	7.826774	23	0.0000001	****
866.723_11.11	rel_abund_log2	b1	b3	24	24	-3.057584	23	0.0055800	**
868.7389_11.19	rel_abund_log2	b1	b3	24	24	-4.654187	23	0.0001100	***
869.6734_6.444	rel_abund_log2	b1	b3	24	24	-2.123826	23	0.0447000	*
869.6738_6.894	rel_abund_log2	b1	b3	24	24	-2.665499	23	0.0138000	*
870.6336_9.039	rel_abund_log2	b1	b3	24	24	-5.900536	23	0.0000051	****
870.7558_11.296	rel_abund_log2	b1	b3	24	24	-5.608460	23	0.0000104	****
871.6776_11.111	rel_abund_log2	b1	b3	24	24	-3.323479	23	0.0029600	**
871.6892_7.141	rel_abund_log2	b1	b3	24	24	3.508699	23	0.0018900	**
872.6526_8.477	rel_abund_log2	b1	b3	24	24	-4.873664	23	0.0000638	****
872.652_8.777	rel_abund_log2	b1	b3	24	24	-5.880113	23	0.0000054	****
872.7324_11.418	rel_abund_log2	b1	b3	24	24	-2.282385	23	0.0320000	*
872.7731_11.418	rel_abund_log2	b1	b3	24	24	-5.209801	23	0.0000278	****
873.7045_7.52	rel_abund_log2	b1	b3	24	24	-4.726576	23	0.0000920	****
874.5538_7.197	rel_abund_log2	b1	b3	24	24	2.489022	23	0.0205000	*
874.6655_10.042	rel_abund_log2	b1	b3	24	24	-3.831711	23	0.0008540	***
874.7461_11.559	rel_abund_log2	b1	b3	24	24	-2.256692	23	0.0338000	*
874.7892_11.559	rel_abund_log2	b1	b3	24	24	-2.341595	23	0.0282000	*
875.7095_11.292	rel_abund_log2	b1	b3	24	24	-5.684946	23	0.0000087	****
875.7207_8.87	rel_abund_log2	b1	b3	24	24	3.517768	23	0.0018500	**
876.5695_7.88	rel_abund_log2	b1	b3	24	24	4.821973	23	0.0000726	****
876.5697_8.13	rel_abund_log2	b1	b3	24	24	2.496049	23	0.0202000	*
876.6835_10.245	rel_abund_log2	b1	b3	24	24	-7.038953	23	0.0000004	****
877.6374_9.86	rel_abund_log2	b1	b3	24	24	5.198234	23	0.0000286	****
877.6374_9.988	rel_abund_log2	b1	b3	24	24	3.527718	23	0.0018000	**
877.7253_11.418	rel_abund_log2	b1	b3	24	24	2.447008	23	0.0225000	*
877.7_7.713	rel_abund_log2	b1	b3	24	24	3.888769	23	0.0007410	***
878.5852_8.857	rel_abund_log2	b1	b3	24	24	3.678875	23	0.0012400	**
878.7033_7.717	rel_abund_log2	b1	b3	24	24	4.397711	23	0.0002090	***
879.7408_11.578	rel_abund_log2	b1	b3	24	24	2.194336	23	0.0386000	*
880.718_11.248	rel_abund_log2	b1	b3	24	24	-4.376792	23	0.0002200	***
881.6374_6.724	rel_abund_log2	b1	b3	24	24	-2.197603	23	0.0383000	*
883.6892_7.419	rel_abund_log2	b1	b3	24	24	-7.528574	23	0.0000001	****
883.6894_6.894	rel_abund_log2	b1	b3	24	24	-3.529922	23	0.0017900	**
884.7697_11.358	rel_abund_log2	b1	b3	24	24	-4.637726	23	0.0001150	***
886.7858_11.492	rel_abund_log2	b1	b3	24	24	-4.346488	23	0.0002370	***
887.6837_6.371	rel_abund_log2	b1	b3	24	24	-2.069430	23	0.0499000	*
887.7201_8.662	rel_abund_log2	b1	b3	24	24	-2.531450	23	0.0187000	*
889.6375_9.586	rel_abund_log2	b1	b3	24	24	-2.249721	23	0.0343000	*
889.6996_7.495	rel_abund_log2	b1	b3	24	24	-2.497189	23	0.0201000	*
889.7358_8.865	rel_abund_log2	b1	b3	24	24	3.108084	23	0.0049500	**
891.7156_7.718	rel_abund_log2	b1	b3	24	24	4.507412	23	0.0001590	***
891.7406_11.489	rel_abund_log2	b1	b3	24	24	-3.703443	23	0.0011700	**
892.7383_11.126	rel_abund_log2	b1	b3	24	24	-3.555903	23	0.0016800	**
893.6733_6.713	rel_abund_log2	b1	b3	24	24	-2.456593	23	0.0220000	*
893.7563_11.645	rel_abund_log2	b1	b3	24	24	-3.429985	23	0.0022900	**
894.7548_11.21	rel_abund_log2	b1	b3	24	24	-4.490365	23	0.0001660	***
896.5379_6.373	rel_abund_log2	b1	b3	24	24	-2.425185	23	0.0236000	*
896.7714_11.316	rel_abund_log2	b1	b3	24	24	-4.704403	23	0.0000972	****
897.7047_7.419	rel_abund_log2	b1	b3	24	24	-7.790842	23	0.0000001	****
897.7049_6.893	rel_abund_log2	b1	b3	24	24	-2.817656	23	0.0097700	**
897.705_7.958	rel_abund_log2	b1	b3	24	24	-3.277787	23	0.0033000	**
898.7872_11.44	rel_abund_log2	b1	b3	24	24	-2.973604	23	0.0068000	**
899.684_6.44	rel_abund_log2	b1	b3	24	24	-2.228754	23	0.0359000	*
899.7091_11.203	rel_abund_log2	b1	b3	24	24	-5.540967	23	0.0000123	****
899.7204_7.586	rel_abund_log2	b1	b3	24	24	-2.377235	23	0.0261000	*
899.7208_8.396	rel_abund_log2	b1	b3	24	24	-2.562957	23	0.0174000	*
900.6836_9.96	rel_abund_log2	b1	b3	24	24	-4.653275	23	0.0001100	***
901.6999_7.143	rel_abund_log2	b1	b3	24	24	4.613462	23	0.0001220	***
901.7251_11.318	rel_abund_log2	b1	b3	24	24	-3.802022	23	0.0009190	***
902.5743_7.805	rel_abund_log2	b1	b3	24	24	-2.419880	23	0.0238000	*
902.7673_8.957	rel_abund_log2	b1	b3	24	24	3.294921	23	0.0031700	**
902.8182_11.738	rel_abund_log2	b1	b3	24	24	3.055876	23	0.0056000	**
903.6531_10.207	rel_abund_log2	b1	b3	24	24	2.614281	23	0.0155000	*
903.6531_9.918	rel_abund_log2	b1	b3	24	24	-3.688009	23	0.0012200	**
904.7835_9.894	rel_abund_log2	b1	b3	24	24	2.684184	23	0.0132000	*
904.8336_11.91	rel_abund_log2	b1	b3	24	24	3.839866	23	0.0008370	***
907.6886_7.2	rel_abund_log2	b1	b3	24	24	-2.678294	23	0.0134000	*
907.7722_11.767	rel_abund_log2	b1	b3	24	24	2.145514	23	0.0427000	*
911.5614_9.119	rel_abund_log2	b1	b3	24	24	2.453120	23	0.0222000	*
911.7203_7.674	rel_abund_log2	b1	b3	24	24	-2.882662	23	0.0084000	**
911.7206_7.958	rel_abund_log2	b1	b3	24	24	-3.466535	23	0.0020900	**
912.8727_11.58	rel_abund_log2	b1	b3	24	24	4.574625	23	0.0001340	***
913.7362_8.395	rel_abund_log2	b1	b3	24	24	-2.850891	23	0.0090400	**
915.7154_7.143	rel_abund_log2	b1	b3	24	24	3.833706	23	0.0008490	***
916.7378_11.078	rel_abund_log2	b1	b3	24	24	-2.613855	23	0.0155000	*
918.754_11.178	rel_abund_log2	b1	b3	24	24	-3.659181	23	0.0013100	**
918.7987_9.897	rel_abund_log2	b1	b3	24	24	3.056147	23	0.0056000	**
919.7465_8.87	rel_abund_log2	b1	b3	24	24	3.667840	23	0.0012800	**
920.75_8.87	rel_abund_log2	b1	b3	24	24	2.630550	23	0.0149000	*
920.7705_11.302	rel_abund_log2	b1	b3	24	24	-2.692494	23	0.0130000	*
922.7862_11.386	rel_abund_log2	b1	b3	24	24	-3.565127	23	0.0016400	**
923.684_6.374	rel_abund_log2	b1	b3	24	24	-3.056209	23	0.0056000	**
923.7092_11.174	rel_abund_log2	b1	b3	24	24	-2.676132	23	0.0135000	*
924.8016_11.511	rel_abund_log2	b1	b3	24	24	-3.657048	23	0.0013100	**
926.8159_11.647	rel_abund_log2	b1	b3	24	24	-2.479754	23	0.0209000	*
927.7399_11.374	rel_abund_log2	b1	b3	24	24	-2.413198	23	0.0242000	*
928.7831_8.964	rel_abund_log2	b1	b3	24	24	-3.885772	23	0.0007470	***
929.7558_11.499	rel_abund_log2	b1	b3	24	24	-2.417969	23	0.0239000	*
930.7988_10.228	rel_abund_log2	b1	b3	24	24	3.246294	23	0.0035600	**
930.7989_9.903	rel_abund_log2	b1	b3	24	24	-3.045202	23	0.0057500	**
930.8479_11.919	rel_abund_log2	b1	b3	24	24	3.548618	23	0.0017100	**
931.7715_11.646	rel_abund_log2	b1	b3	24	24	-3.509229	23	0.0018900	**
937.6995_6.371	rel_abund_log2	b1	b3	24	24	-4.746088	23	0.0000877	****
938.8159_11.193	rel_abund_log2	b1	b3	24	24	-4.654542	23	0.0001100	***
941.7308_7.633	rel_abund_log2	b1	b3	24	24	-3.652261	23	0.0013300	**
941.7313_7.957	rel_abund_log2	b1	b3	24	24	-3.813549	23	0.0008930	***
942.7538_11.123	rel_abund_log2	b1	b3	24	24	-2.402254	23	0.0248000	*
942.7986_8.961	rel_abund_log2	b1	b3	24	24	-2.410714	23	0.0243000	*
944.7698_11.221	rel_abund_log2	b1	b3	24	24	-3.344451	23	0.0028100	**
944.8145_9.903	rel_abund_log2	b1	b3	24	24	-3.171629	23	0.0042600	**
944.8656_11.558	rel_abund_log2	b1	b3	24	24	-2.367638	23	0.0267000	*
949.7248_11.221	rel_abund_log2	b1	b3	24	24	-3.237497	23	0.0036400	**
950.8156_11.512	rel_abund_log2	b1	b3	24	24	-3.303901	23	0.0031000	**
951.7153_7.77	rel_abund_log2	b1	b3	24	24	-3.845491	23	0.0008250	***
952.831_11.66	rel_abund_log2	b1	b3	24	24	-2.636550	23	0.0147000	*
953.7312_8.446	rel_abund_log2	b1	b3	24	24	-2.275111	23	0.0325000	*
953.7558_11.474	rel_abund_log2	b1	b3	24	24	2.402817	23	0.0247000	*
955.7465_8.781	rel_abund_log2	b1	b3	24	24	-3.389033	23	0.0025200	**
957.7865_11.655	rel_abund_log2	b1	b3	24	24	-2.544370	23	0.0181000	*
963.8345_12.135	rel_abund_log2	b1	b3	24	24	2.395330	23	0.0251000	*
965.7308_7.761	rel_abund_log2	b1	b3	24	24	-3.527716	23	0.0018000	**
966.8463_11.305	rel_abund_log2	b1	b3	24	24	-3.170502	23	0.0042700	**
968.863_11.436	rel_abund_log2	b1	b3	24	24	-2.230263	23	0.0358000	*
972.7339_9.773	rel_abund_log2	b1	b3	24	24	2.710553	23	0.0125000	*
972.8_11.345	rel_abund_log2	b1	b3	24	24	-2.649783	23	0.0143000	*
976.684_9.115	rel_abund_log2	b1	b3	24	24	2.333113	23	0.0287000	*
978.8456_11.66	rel_abund_log2	b1	b3	24	24	-2.233451	23	0.0355000	*
993.7984_10.238	rel_abund_log2	b1	b3	24	24	-4.313462	23	0.0002580	***

# how many are significant?
nrow(tomato_t.test_paired_sig)

## [1] 786

Remove BG diet

# let's grab both datasets for features sig in pre v post red and pre v post control and combine them
fulljoin_t.test_tomANDctrl <- full_join(tomato_t.test_paired_sig, ctrl_t.test_paired_sig,
                                        by = "mz_rt",
                                        suffix = c(".tom", ".ctrl"))

# number features in full list
nrow(fulljoin_t.test_tomANDctrl)

## [1] 897

Let’s try and remove features significant due to the background diet

sig_paired_tom_rmBG <- fulljoin_t.test_tomANDctrl %>%
  # add a column to account for direction
  mutate(sign = statistic.tom * statistic.ctrl) %>%
  # replace NAs in the sign column with 0
  mutate(sign = replace_na(sign, 0)) %>%
  # replace NAs in the statistic.ctrl column to 0
  mutate(statistic.ctrl = replace_na(statistic.ctrl, 0)) %>%
  # filter for columns that are either negative (means ctrl and tomato are going in opposite dir) or where the stat.ctrl col is 0 (so we don't remove features that are just not present in the sig control list)
  filter((sign < 0 | statistic.ctrl == 0))

# number of features in new list without bg diet effect
nrow(sig_paired_tom_rmBG)

## [1] 460

How many BG-diet-related features did we remove from the list of significant beta features?

nrow(tomato_t.test_paired_sig) - nrow(sig_paired_tom_rmBG)

## [1] 326

ANOVA overlap

Keep sig features in t-test that have a match in sig ANOVA. Let’s take our new feature list (background diet effect removed)

# select only features from paired list that have a match in ANOVA list
tom_sig_ANOVA_overlap_paired <- inner_join(sig_paired_tom_rmBG,
                                            trt_tukeyHSD_sig,
                                            by = "mz_rt",
                                            suffix = c(".t-test", ".tukeys"))

# features overlapping with sig ANOVA
unique(tom_sig_ANOVA_overlap_paired$mz_rt)

##  [1] "1157.3434_6.852"  "1341.2177_11.895" "1342.2211_11.896" "1479.1185_7.024" 
##  [5] "1526.0908_8.976"  "1527.094_8.977"   "1536.6252_6.856"  "1619.2973_8.957" 
##  [9] "1624.8418_9.901"  "1635.8345_9.901"  "1649.3448_9.904"  "286.2013_0.633"  
## [13] "337.1048_1.268"   "536.437_11.201"   "605.5497_9.629"   "608.4637_5.649"  
## [17] "677.5586_5.973"   "726.5428_8.237"   "738.543_8.181"    "748.5258_8.233"  
## [21] "759.0903_6.983"   "759.1221_6.983"   "774.5404_8.982"   "785.0201_6.879"  
## [25] "796.5222_9.007"   "797.6453_8.401"   "811.6598_8.009"   "813.6866_9.902"  
## [29] "827.6999_10.389"  "835.6566_8.968"   "851.64_9.902"     "865.5795_9.626"  
## [33] "876.5695_7.88"    "878.5852_8.857"   "889.6375_9.586"   "897.705_7.958"   
## [37] "903.6531_10.207"  "907.7722_11.767"

Here, I will compare control to each tomato treatment individually, and then tomato treatments against each other. I will also compare tomato to control. I am using the log transformed values of rel abundance since parametric tests assume normality.

Red v ctrl

# run t-test
red_v_ctrl_t.test <- df_for_stats %>%
  filter(treatment %in% c("control", "red")) %>%
  filter(period == "b3") %>%
  dplyr::select(subject, treatment, mz_rt, rel_abund_log2) %>%
  group_by(mz_rt) %>%
  t_test(rel_abund_log2 ~ treatment, 
         paired = FALSE, 
         p.adjust.method = "BH") %>% # Benjamini-Hochberg controlling to lower false positives
  add_significance()

Statistically significant features

# which features are significant?
sig_red_v_ctrl_t.test <- red_v_ctrl_t.test %>%
  filter(p < 0.05)
kable(sig_red_v_ctrl_t.test)

mz_rt	.y.	group1	group2	n1	n2	statistic	df	p	p.signif
1186.3509_7.119	rel_abund_log2	control	red	11	12	-2.127690	20.99967	0.04540	*
1226.4736_8.96	rel_abund_log2	control	red	11	12	2.915862	20.99028	0.00826	**
1329.2228_12.335	rel_abund_log2	control	red	11	12	2.228528	19.22203	0.03800	*
1522.0815_7.299	rel_abund_log2	control	red	11	12	-2.202931	20.08307	0.03940	*
1551.6195_6.93	rel_abund_log2	control	red	11	12	-2.158220	20.99914	0.04260	*
1552.6273_7.034	rel_abund_log2	control	red	11	12	2.245082	17.03950	0.03830	*
1594.0976_6.869	rel_abund_log2	control	red	11	12	2.113064	20.98841	0.04670	*
1595.6827_8.019	rel_abund_log2	control	red	11	12	-2.611962	20.65333	0.01640	*
1597.3162_8.956	rel_abund_log2	control	red	11	12	2.147835	20.96746	0.04360	*
1607.1867_8.003	rel_abund_log2	control	red	11	12	-2.805197	20.96735	0.01060	*
1612.335_9.9	rel_abund_log2	control	red	11	12	2.328866	20.79227	0.03000	*
162.1126_0.545	rel_abund_log2	control	red	11	12	3.511107	20.93913	0.00208	**
1685.4637_11.522	rel_abund_log2	control	red	11	12	-2.617311	19.45746	0.01670	*
184.0946_0.54	rel_abund_log2	control	red	11	12	2.721035	20.57593	0.01290	*
330.2637_0.689	rel_abund_log2	control	red	11	12	-2.130643	16.71068	0.04830	*
369.3508_10.879	rel_abund_log2	control	red	11	12	2.499518	20.72949	0.02090	*
383.1533_0.931	rel_abund_log2	control	red	11	12	-2.516115	16.30960	0.02270	*
515.313_3.077	rel_abund_log2	control	red	11	12	2.379443	19.73893	0.02750	*
536.437_10.971	rel_abund_log2	control	red	11	12	-17.577022	20.56390	0.00000	****
698.6194_10.879	rel_abund_log2	control	red	11	12	2.818125	15.64467	0.01260	*
744.5538_6.51	rel_abund_log2	control	red	11	12	-2.190512	17.81496	0.04200	*
759.1449_6.983	rel_abund_log2	control	red	11	12	-3.104064	20.80020	0.00541	**
759.243_6.983	rel_abund_log2	control	red	11	12	-2.114587	20.52142	0.04690	*
759.2652_6.984	rel_abund_log2	control	red	11	12	-2.202204	20.68103	0.03910	*
759.4259_6.982	rel_abund_log2	control	red	11	12	-2.177128	20.98581	0.04100	*
760.4052_6.983	rel_abund_log2	control	red	11	12	-2.309024	15.84633	0.03480	*
760.4925_6.98	rel_abund_log2	control	red	11	12	-3.003788	20.02325	0.00701	**
774.5634_4.81	rel_abund_log2	control	red	11	12	-3.173353	20.99943	0.00458	**
783.431_6.876	rel_abund_log2	control	red	11	12	2.162299	19.61778	0.04310	*
784.6324_6.878	rel_abund_log2	control	red	11	12	2.312621	12.94912	0.03780	*
787.0988_8.052	rel_abund_log2	control	red	11	12	-2.254153	20.96946	0.03500	*
793.5586_7.102	rel_abund_log2	control	red	11	12	2.085902	20.49242	0.04970	*
797.5914_8.07	rel_abund_log2	control	red	11	12	-2.487848	15.23107	0.02490	*
826.594_5.988	rel_abund_log2	control	red	11	12	-3.865591	15.83786	0.00139	**
836.6167_8.053	rel_abund_log2	control	red	11	12	2.348559	20.98178	0.02870	*
844.6529_8.109	rel_abund_log2	control	red	11	12	-3.094096	17.15228	0.00653	**
928.7831_8.964	rel_abund_log2	control	red	11	12	2.129956	20.87973	0.04520	*

# how many are significant?
nrow(sig_red_v_ctrl_t.test)

## [1] 37

ANOVA overlap

Keep sig features in unpaired t-test that have a match in sig ANOVA

sig_overlap_ctrl_red <- inner_join(sig_red_v_ctrl_t.test,
                                   trt_tukeyHSD_sig,
                                   by = "mz_rt",
                                   suffix = c(".t-test", ".tukeys"))

unique(sig_overlap_ctrl_red$mz_rt)

## [1] "1226.4736_8.96"  "1594.0976_6.869" "162.1126_0.545"  "536.437_10.971" 
## [5] "797.5914_8.07"   "928.7831_8.964"

Beta v ctrl

# run t-tests
beta_v_ctrl_t.test <- df_for_stats %>%
  filter(treatment %in% c("control" , "beta"),
         period == "b3") %>%
  dplyr::select(subject, treatment, mz_rt, rel_abund_log2) %>%
  group_by(mz_rt) %>%
  t_test(rel_abund_log2 ~ treatment, 
         paired = FALSE, 
         p.adjust.method = "BH") %>% # Benjamini-Hochberg controlling to lower false positives
  add_significance()

Statistically significant features

# which features are significant?
sig_beta_v_ctrl_t.test <- beta_v_ctrl_t.test %>%
  filter(p < 0.05)
kable(sig_beta_v_ctrl_t.test)

mz_rt	.y.	group1	group2	n1	n2	statistic	df	p	p.signif
1002.658_3.079	rel_abund_log2	beta	control	12	11	2.192276	20.99753	0.0398000	*
118.0864_0.597	rel_abund_log2	beta	control	12	11	-2.152878	17.28673	0.0457000	*
1192.8412_6.709	rel_abund_log2	beta	control	12	11	-2.096670	19.71279	0.0491000	*
1193.3431_6.71	rel_abund_log2	beta	control	12	11	-2.706443	20.99265	0.0132000	*
1195.8336_6.857	rel_abund_log2	beta	control	12	11	-2.157819	20.46148	0.0430000	*
1197.3456_7.08	rel_abund_log2	beta	control	12	11	-2.397133	19.05306	0.0269000	*
1218.5051_9.902	rel_abund_log2	beta	control	12	11	-2.670035	17.96773	0.0156000	*
1226.4736_8.96	rel_abund_log2	beta	control	12	11	-3.010350	18.94948	0.0072100	**
1227.3893_7.956	rel_abund_log2	beta	control	12	11	-2.771515	19.77305	0.0119000	*
1227.7565_5.834	rel_abund_log2	beta	control	12	11	-2.746757	19.42664	0.0127000	*
1242.5051_10.22	rel_abund_log2	beta	control	12	11	-2.152101	18.18360	0.0451000	*
1242.5053_9.906	rel_abund_log2	beta	control	12	11	-2.450448	19.02528	0.0241000	*
1329.2228_12.335	rel_abund_log2	beta	control	12	11	-2.987544	19.54171	0.0074100	**
1406.1439_6.504	rel_abund_log2	beta	control	12	11	-2.333209	18.30912	0.0312000	*
1417.6332_6.508	rel_abund_log2	beta	control	12	11	-2.444258	14.34033	0.0280000	*
1418.1346_6.507	rel_abund_log2	beta	control	12	11	-2.664070	17.33629	0.0162000	*
1419.1345_6.509	rel_abund_log2	beta	control	12	11	-2.238653	17.49860	0.0384000	*
1429.1272_6.513	rel_abund_log2	beta	control	12	11	-2.465973	20.97621	0.0224000	*
1548.6249_6.858	rel_abund_log2	beta	control	12	11	-2.796636	18.23823	0.0118000	*
1557.1328_6.71	rel_abund_log2	beta	control	12	11	-2.970215	20.99902	0.0073000	**
1558.1357_6.713	rel_abund_log2	beta	control	12	11	-2.224426	20.05466	0.0378000	*
1575.1173_6.863	rel_abund_log2	beta	control	12	11	-2.439213	18.78736	0.0248000	*
1577.1237_6.867	rel_abund_log2	beta	control	12	11	-2.162239	20.30340	0.0427000	*
1585.1303_6.711	rel_abund_log2	beta	control	12	11	-2.322577	18.17618	0.0320000	*
1592.2118_7.764	rel_abund_log2	beta	control	12	11	-2.427547	20.44978	0.0245000	*
1596.1232_6.714	rel_abund_log2	beta	control	12	11	-2.267284	15.09253	0.0385000	*
1596.6247_6.715	rel_abund_log2	beta	control	12	11	-2.352786	14.42196	0.0333000	*
1597.1201_6.716	rel_abund_log2	beta	control	12	11	-2.098926	18.79358	0.0496000	*
1597.3162_8.956	rel_abund_log2	beta	control	12	11	-2.595291	19.82295	0.0174000	*
1601.3486_9.899	rel_abund_log2	beta	control	12	11	-2.189008	16.42417	0.0434000	*
1611.8348_9.901	rel_abund_log2	beta	control	12	11	-2.107998	17.87676	0.0494000	*
1612.335_9.9	rel_abund_log2	beta	control	12	11	-2.392405	17.24316	0.0284000	*
1618.2939_8.956	rel_abund_log2	beta	control	12	11	-3.352964	20.92152	0.0030200	**
1619.2973_8.957	rel_abund_log2	beta	control	12	11	-2.874547	19.99275	0.0093700	**
1624.3414_10.412	rel_abund_log2	beta	control	12	11	-2.169675	20.82741	0.0418000	*
1624.8418_9.901	rel_abund_log2	beta	control	12	11	-2.283605	17.54810	0.0351000	*
1635.8345_9.901	rel_abund_log2	beta	control	12	11	-2.270944	15.87441	0.0374000	*
536.437_10.971	rel_abund_log2	beta	control	12	11	3.020418	20.87237	0.0065400	**
536.437_11.201	rel_abund_log2	beta	control	12	11	7.859672	14.28943	0.0000015	****
568.4266_4.64	rel_abund_log2	beta	control	12	11	-2.922333	20.86424	0.0081800	**
569.4277_4.639	rel_abund_log2	beta	control	12	11	-2.934578	19.21458	0.0084400	**
585.2705_0.643	rel_abund_log2	beta	control	12	11	-2.554432	20.85213	0.0185000	*
593.4762_4.644	rel_abund_log2	beta	control	12	11	-2.605201	19.61083	0.0171000	*
614.3398_3.784	rel_abund_log2	beta	control	12	11	2.197436	20.98167	0.0394000	*
692.5581_7.191	rel_abund_log2	beta	control	12	11	-2.305604	20.72998	0.0316000	*
701.5604_5.818	rel_abund_log2	beta	control	12	11	-2.101908	20.98834	0.0478000	*
703.5745_6.822	rel_abund_log2	beta	control	12	11	-2.205941	19.68302	0.0394000	*
703.5779_6.502	rel_abund_log2	beta	control	12	11	-2.176357	17.33686	0.0436000	*
704.3938_6.501	rel_abund_log2	beta	control	12	11	-2.164504	17.94508	0.0442000	*
704.4038_6.503	rel_abund_log2	beta	control	12	11	-2.785378	17.21802	0.0126000	*
705.6376_6.502	rel_abund_log2	beta	control	12	11	-2.295174	15.32628	0.0362000	*
725.5564_6.829	rel_abund_log2	beta	control	12	11	-2.341706	16.94685	0.0317000	*
741.551_6.153	rel_abund_log2	beta	control	12	11	-2.445215	20.92336	0.0234000	*
742.5718_8.28	rel_abund_log2	beta	control	12	11	-2.215412	20.62847	0.0381000	*
751.5717_6.812	rel_abund_log2	beta	control	12	11	-2.106329	20.70189	0.0476000	*
753.5881_7.505	rel_abund_log2	beta	control	12	11	-2.382275	20.14697	0.0272000	*
765.9874_3.073	rel_abund_log2	beta	control	12	11	2.633339	20.82417	0.0156000	*
774.5404_8.982	rel_abund_log2	beta	control	12	11	3.002996	20.75713	0.0068300	**
781.62_7.043	rel_abund_log2	beta	control	12	11	-2.987588	20.62239	0.0071100	**
784.5042_6.876	rel_abund_log2	beta	control	12	11	-2.647485	16.86826	0.0170000	*
785.6542_8.953	rel_abund_log2	beta	control	12	11	-2.269810	19.95424	0.0345000	*
794.6868_10.627	rel_abund_log2	beta	control	12	11	-2.392037	20.55516	0.0264000	*
796.5222_9.007	rel_abund_log2	beta	control	12	11	2.096164	20.87242	0.0484000	*
797.5914_8.07	rel_abund_log2	beta	control	12	11	2.669145	16.58979	0.0164000	*
799.6692_9.585	rel_abund_log2	beta	control	12	11	-2.586140	20.97352	0.0172000	*
801.686_10.409	rel_abund_log2	beta	control	12	11	-2.243837	18.26507	0.0375000	*
807.6349_8.948	rel_abund_log2	beta	control	12	11	-2.282882	19.84641	0.0336000	*
809.6525_8.065	rel_abund_log2	beta	control	12	11	-2.708238	19.13195	0.0139000	*
810.6564_8.03	rel_abund_log2	beta	control	12	11	-2.492587	16.63166	0.0236000	*
811.6703_8.961	rel_abund_log2	beta	control	12	11	-2.259023	19.99700	0.0352000	*
813.6854_10.226	rel_abund_log2	beta	control	12	11	-2.262063	20.46881	0.0347000	*
824.6527_9.167	rel_abund_log2	beta	control	12	11	-2.805985	20.76907	0.0107000	*
824.6537_9.596	rel_abund_log2	beta	control	12	11	-2.494020	20.48129	0.0213000	*
827.6999_10.502	rel_abund_log2	beta	control	12	11	-2.131073	20.19337	0.0456000	*
830.6471_9.551	rel_abund_log2	beta	control	12	11	-2.360358	20.73100	0.0281000	*
833.6507_8.971	rel_abund_log2	beta	control	12	11	-2.992813	20.97487	0.0069400	**
834.7053_6.506	rel_abund_log2	beta	control	12	11	-2.134743	20.98130	0.0447000	*
835.6566_8.968	rel_abund_log2	beta	control	12	11	-2.316831	20.12171	0.0312000	*
835.6664_10.22	rel_abund_log2	beta	control	12	11	-2.460218	18.89251	0.0237000	*
862.6247_6.471	rel_abund_log2	beta	control	12	11	-2.193547	19.96398	0.0403000	*
902.7673_8.957	rel_abund_log2	beta	control	12	11	-2.131871	20.93510	0.0450000	*
928.7831_8.964	rel_abund_log2	beta	control	12	11	-2.395480	18.25770	0.0275000	*
946.718_9.767	rel_abund_log2	beta	control	12	11	-2.356107	18.21479	0.0299000	*
970.718_8.858	rel_abund_log2	beta	control	12	11	-2.205551	20.58688	0.0389000	*

# how many are significant?
nrow(sig_beta_v_ctrl_t.test)

## [1] 84

ANOVA overlap

Keep sig features in t-test that have a match in sig ANOVA

sig_overlap_ctrl_beta <- inner_join(sig_beta_v_ctrl_t.test,
                                   trt_tukeyHSD_sig,
                                   by = "mz_rt",
                                   suffix = c(".t-test", ".tukeys"))

# which features overlap?
unique(sig_overlap_ctrl_beta$mz_rt)

##  [1] "118.0864_0.597"   "1192.8412_6.709"  "1193.3431_6.71"   "1226.4736_8.96"  
##  [5] "1242.5051_10.22"  "1242.5053_9.906"  "1548.6249_6.858"  "1557.1328_6.71"  
##  [9] "1558.1357_6.713"  "1597.1201_6.716"  "1618.2939_8.956"  "1619.2973_8.957" 
## [13] "1624.3414_10.412" "1624.8418_9.901"  "1635.8345_9.901"  "536.437_10.971"  
## [17] "536.437_11.201"   "593.4762_4.644"   "692.5581_7.191"   "742.5718_8.28"   
## [21] "774.5404_8.982"   "781.62_7.043"     "785.6542_8.953"   "796.5222_9.007"  
## [25] "797.5914_8.07"    "799.6692_9.585"   "801.686_10.409"   "807.6349_8.948"  
## [29] "809.6525_8.065"   "810.6564_8.03"    "811.6703_8.961"   "813.6854_10.226" 
## [33] "827.6999_10.502"  "833.6507_8.971"   "835.6566_8.968"   "835.6664_10.22"  
## [37] "902.7673_8.957"   "928.7831_8.964"

Beta v red

# run t-tests
beta_v_red_t.test <- df_for_stats %>%
  filter(treatment %in% c("beta", "red"),
         period == "b3") %>%
  dplyr::select(subject, treatment, mz_rt, rel_abund_log2) %>%
  group_by(mz_rt) %>%
  t_test(rel_abund_log2 ~ treatment, 
         paired = FALSE, 
         p.adjust.method = "BH") %>% # Benjamini-Hochberg controlling to lower false positives
  add_significance()

Statistically significant features

# which features are significant?
sig_beta_v_red_t.test <- beta_v_red_t.test %>%
  filter(p < 0.05)
kable(sig_beta_v_red_t.test)

mz_rt	.y.	group1	group2	n1	n2	statistic	df	p	p.signif
1002.658_3.079	rel_abund_log2	beta	red	12	12	2.942650	21.85481	0.00756	**
1157.3434_6.852	rel_abund_log2	beta	red	12	12	-2.716960	20.28153	0.01320	*
1185.344_7.069	rel_abund_log2	beta	red	12	12	2.360789	20.80053	0.02810	*
1227.7565_5.834	rel_abund_log2	beta	red	12	12	-2.321969	20.41165	0.03070	*
1249.7383_5.838	rel_abund_log2	beta	red	12	12	-2.544957	21.27762	0.01870	*
1250.8236_3.081	rel_abund_log2	beta	red	12	12	2.168699	21.49554	0.04150	*
1363.015_6.835	rel_abund_log2	beta	red	12	12	-2.655713	19.88652	0.01520	*
1511.145_6.854	rel_abund_log2	beta	red	12	12	-2.263250	18.81052	0.03560	*
1536.6252_6.856	rel_abund_log2	beta	red	12	12	-2.213067	19.08980	0.03930	*
1542.2142_6.931	rel_abund_log2	beta	red	12	12	-2.458452	18.58234	0.02400	*
1551.6195_6.93	rel_abund_log2	beta	red	12	12	-2.381095	20.04404	0.02730	*
1583.089_7.72	rel_abund_log2	beta	red	12	12	-2.147913	18.49558	0.04520	*
1607.1867_8.003	rel_abund_log2	beta	red	12	12	-2.435178	21.22758	0.02380	*
1625.2172_8.052	rel_abund_log2	beta	red	12	12	2.337877	19.35722	0.03030	*
346.0093_0.919	rel_abund_log2	beta	red	12	12	-2.255192	21.92703	0.03440	*
508.3578_0.689	rel_abund_log2	beta	red	12	12	-2.411917	20.00919	0.02560	*
515.313_3.077	rel_abund_log2	beta	red	12	12	3.377368	17.13443	0.00355	**
518.322_3.062	rel_abund_log2	beta	red	12	12	-2.499157	17.98059	0.02240	*
536.437_10.971	rel_abund_log2	beta	red	12	12	-14.700188	21.90006	0.00000	****
536.437_11.201	rel_abund_log2	beta	red	12	12	8.822292	18.46199	0.00000	****
541.3858_4.202	rel_abund_log2	beta	red	12	12	-2.875875	20.40262	0.00922	**
597.4596_3.082	rel_abund_log2	beta	red	12	12	2.091192	21.38103	0.04860	*
692.5581_7.191	rel_abund_log2	beta	red	12	12	-2.420822	21.98562	0.02420	*
699.5412_5.971	rel_abund_log2	beta	red	12	12	-2.368948	20.58067	0.02770	*
717.5903_7.018	rel_abund_log2	beta	red	12	12	-2.375782	20.94224	0.02710	*
748.6211_9.504	rel_abund_log2	beta	red	12	12	-3.402255	21.95397	0.00256	**
750.52_0.687	rel_abund_log2	beta	red	12	12	-2.276167	19.67075	0.03420	*
759.058_6.985	rel_abund_log2	beta	red	12	12	-2.616382	21.69609	0.01590	*
759.2209_6.98	rel_abund_log2	beta	red	12	12	-2.323769	21.16707	0.03020	*
760.2926_6.982	rel_abund_log2	beta	red	12	12	-2.083027	21.99999	0.04910	*
760.4052_6.983	rel_abund_log2	beta	red	12	12	-3.496168	19.14834	0.00239	**
760.4788_6.981	rel_abund_log2	beta	red	12	12	-2.194262	21.76911	0.03920	*
760.4925_6.98	rel_abund_log2	beta	red	12	12	-2.721056	20.35010	0.01300	*
788.3339_8.051	rel_abund_log2	beta	red	12	12	2.114345	20.48855	0.04690	*
800.5558_9.058	rel_abund_log2	beta	red	12	12	2.595942	19.14165	0.01770	*
805.6187_7.848	rel_abund_log2	beta	red	12	12	-2.086894	21.70686	0.04890	*
806.973_6.716	rel_abund_log2	beta	red	12	12	-2.258215	18.55660	0.03620	*
820.6364_8.966	rel_abund_log2	beta	red	12	12	-2.132966	20.55453	0.04520	*
824.6537_9.596	rel_abund_log2	beta	red	12	12	-2.863140	21.80253	0.00909	**
825.6815_9.335	rel_abund_log2	beta	red	12	12	-2.158661	21.89567	0.04210	*
830.6471_9.551	rel_abund_log2	beta	red	12	12	-2.230766	21.50994	0.03650	*
834.5933_5.658	rel_abund_log2	beta	red	12	12	-2.392944	21.21013	0.02600	*
842.638_7.268	rel_abund_log2	beta	red	12	12	-2.246069	21.90116	0.03510	*
846.6771_7.001	rel_abund_log2	beta	red	12	12	2.693786	20.42515	0.01380	*
889.7001_7.009	rel_abund_log2	beta	red	12	12	-2.470332	17.28952	0.02420	*
890.7034_7.007	rel_abund_log2	beta	red	12	12	-2.185075	18.79360	0.04180	*
939.7152_7.328	rel_abund_log2	beta	red	12	12	2.129918	18.89856	0.04650	*
972.8574_11.264	rel_abund_log2	beta	red	12	12	-2.817938	21.12523	0.01030	*

# how many are significant?
nrow(sig_beta_v_red_t.test)

## [1] 48

ANOVA overlap

Keep sig features in t-test that have a match in sig ANOVA

sig_overlap_beta_red <- inner_join(sig_beta_v_red_t.test,
                                   trt_tukeyHSD_sig,
                                   by = "mz_rt",
                                   suffix = c(".t-test", ".tukeys"))

# which features overlap?
unique(sig_overlap_beta_red$mz_rt)

##  [1] "1157.3434_6.852" "1363.015_6.835"  "1511.145_6.854"  "1536.6252_6.856"
##  [5] "536.437_10.971"  "536.437_11.201"  "692.5581_7.191"  "699.5412_5.971" 
##  [9] "717.5903_7.018"  "805.6187_7.848"  "825.6815_9.335"  "834.5933_5.658"

Tomato v ctrl

# run t-tests
tom_v_ctrl_t.test <- df_for_stats %>%
  filter(tomato_or_control %in% c("control", "tomato"),
         period == "b3") %>%
  dplyr::select(subject, tomato_or_control, mz_rt, rel_abund_log2) %>%
  group_by(mz_rt) %>%
  t_test(rel_abund_log2 ~ tomato_or_control, 
         paired = FALSE, 
         p.adjust.method = "BH") %>% # Benjamini-Hochberg controlling to lower false positives
  add_significance()

Statistically significant features

# which features are significant?
sig_tom_v_ctrl_t.test <- tom_v_ctrl_t.test %>%
  filter(p < 0.05)
kable(sig_tom_v_ctrl_t.test)

mz_rt	.y.	group1	group2	n1	n2	statistic	df	p	p.signif
1192.8412_6.709	rel_abund_log2	control	tomato	11	24	2.428666	27.80026	0.0219000	*
1193.3431_6.71	rel_abund_log2	control	tomato	11	24	2.213639	24.43580	0.0364000	*
1195.8336_6.857	rel_abund_log2	control	tomato	11	24	2.160327	23.42098	0.0412000	*
1197.3456_7.08	rel_abund_log2	control	tomato	11	24	2.505757	28.07853	0.0183000	*
1218.5051_9.902	rel_abund_log2	control	tomato	11	24	2.677839	27.39888	0.0124000	*
1226.4736_8.96	rel_abund_log2	control	tomato	11	24	3.586004	25.57097	0.0013900	**
1227.3893_7.956	rel_abund_log2	control	tomato	11	24	2.518309	32.77798	0.0169000	*
1242.5051_10.22	rel_abund_log2	control	tomato	11	24	2.115289	20.22145	0.0470000	*
1329.2228_12.335	rel_abund_log2	control	tomato	11	24	3.212661	27.64360	0.0033300	**
1418.1346_6.507	rel_abund_log2	control	tomato	11	24	2.620702	29.75576	0.0137000	*
1429.1272_6.513	rel_abund_log2	control	tomato	11	24	2.124134	21.59777	0.0454000	*
1469.1116_6.5	rel_abund_log2	control	tomato	11	24	-2.169593	31.45420	0.0377000	*
1535.1521_6.713	rel_abund_log2	control	tomato	11	24	2.232633	32.18676	0.0327000	*
1548.6249_6.858	rel_abund_log2	control	tomato	11	24	2.275686	29.80436	0.0302000	*
1552.6273_7.034	rel_abund_log2	control	tomato	11	24	2.503646	30.66231	0.0178000	*
1557.1328_6.71	rel_abund_log2	control	tomato	11	24	2.406573	26.10839	0.0235000	*
1577.1237_6.867	rel_abund_log2	control	tomato	11	24	2.522247	23.02015	0.0190000	*
1585.1303_6.711	rel_abund_log2	control	tomato	11	24	2.237797	31.09485	0.0325000	*
1591.2114_7.764	rel_abund_log2	control	tomato	11	24	2.163665	26.26867	0.0398000	*
1592.2118_7.764	rel_abund_log2	control	tomato	11	24	2.725596	29.30585	0.0107000	*
1595.6827_8.019	rel_abund_log2	control	tomato	11	24	-2.143765	23.47342	0.0426000	*
1596.1232_6.714	rel_abund_log2	control	tomato	11	24	2.274823	31.90041	0.0298000	*
1596.6247_6.715	rel_abund_log2	control	tomato	11	24	2.237408	32.65040	0.0322000	*
1597.3162_8.956	rel_abund_log2	control	tomato	11	24	2.861105	24.38081	0.0085300	**
1601.3486_9.899	rel_abund_log2	control	tomato	11	24	2.388334	29.93041	0.0234000	*
1611.8348_9.901	rel_abund_log2	control	tomato	11	24	2.206186	26.65447	0.0362000	*
1612.335_9.9	rel_abund_log2	control	tomato	11	24	2.858418	27.76094	0.0079800	**
1618.2939_8.956	rel_abund_log2	control	tomato	11	24	3.147730	22.29351	0.0046200	**
1619.2973_8.957	rel_abund_log2	control	tomato	11	24	2.878082	25.25003	0.0080300	**
162.1126_0.545	rel_abund_log2	control	tomato	11	24	3.195301	23.73628	0.0039200	**
1622.3263_9.899	rel_abund_log2	control	tomato	11	24	2.295479	26.07570	0.0300000	*
1624.3414_10.412	rel_abund_log2	control	tomato	11	24	2.371611	18.11148	0.0290000	*
1624.8418_9.901	rel_abund_log2	control	tomato	11	24	2.159564	27.59775	0.0397000	*
1635.8345_9.901	rel_abund_log2	control	tomato	11	24	2.741381	31.26326	0.0100000	**
1636.3352_9.901	rel_abund_log2	control	tomato	11	24	2.369464	32.89143	0.0238000	*
1685.4637_11.522	rel_abund_log2	control	tomato	11	24	-2.196385	18.19751	0.0413000	*
184.0946_0.54	rel_abund_log2	control	tomato	11	24	2.309608	23.26080	0.0301000	*
353.079_1.243	rel_abund_log2	control	tomato	11	24	-2.114681	23.04164	0.0455000	*
369.3508_10.879	rel_abund_log2	control	tomato	11	24	2.638153	22.64076	0.0148000	*
383.1533_0.931	rel_abund_log2	control	tomato	11	24	-2.456402	31.28042	0.0198000	*
536.437_10.971	rel_abund_log2	control	tomato	11	24	-5.922077	30.78663	0.0000016	****
536.437_11.201	rel_abund_log2	control	tomato	11	24	-3.500544	26.12831	0.0016900	**
568.4266_4.64	rel_abund_log2	control	tomato	11	24	2.706363	22.15726	0.0128000	*
569.4277_4.639	rel_abund_log2	control	tomato	11	24	3.120319	29.53913	0.0040100	**
585.2705_0.643	rel_abund_log2	control	tomato	11	24	2.467032	18.70598	0.0235000	*
593.4762_4.644	rel_abund_log2	control	tomato	11	24	2.180902	28.00726	0.0377000	*
698.6194_10.879	rel_abund_log2	control	tomato	11	24	2.501330	14.75756	0.0247000	*
704.3938_6.501	rel_abund_log2	control	tomato	11	24	2.555392	26.92491	0.0166000	*
704.4038_6.503	rel_abund_log2	control	tomato	11	24	2.294970	31.10600	0.0286000	*
705.6376_6.502	rel_abund_log2	control	tomato	11	24	2.224025	31.82409	0.0334000	*
744.5538_6.51	rel_abund_log2	control	tomato	11	24	-2.528446	30.83301	0.0168000	*
759.1449_6.983	rel_abund_log2	control	tomato	11	24	-2.513737	22.08206	0.0197000	*
765.9874_3.073	rel_abund_log2	control	tomato	11	24	-2.670730	24.23921	0.0133000	*
774.5404_8.982	rel_abund_log2	control	tomato	11	24	-2.326847	24.14383	0.0287000	*
774.5634_4.81	rel_abund_log2	control	tomato	11	24	-2.112777	28.72279	0.0434000	*
781.62_7.043	rel_abund_log2	control	tomato	11	24	2.325685	19.18802	0.0311000	*
783.431_6.876	rel_abund_log2	control	tomato	11	24	2.212887	26.71419	0.0356000	*
784.5042_6.876	rel_abund_log2	control	tomato	11	24	2.378216	30.74739	0.0238000	*
784.6324_6.878	rel_abund_log2	control	tomato	11	24	2.438229	32.27817	0.0204000	*
785.6542_8.953	rel_abund_log2	control	tomato	11	24	2.460846	24.57018	0.0213000	*
794.6868_10.627	rel_abund_log2	control	tomato	11	24	2.481969	19.68921	0.0222000	*
794.7051_10.958	rel_abund_log2	control	tomato	11	24	2.249156	32.96811	0.0313000	*
797.5914_8.07	rel_abund_log2	control	tomato	11	24	-2.758682	13.17839	0.0161000	*
799.6692_9.585	rel_abund_log2	control	tomato	11	24	2.296770	22.88124	0.0311000	*
801.686_10.409	rel_abund_log2	control	tomato	11	24	2.064494	26.70938	0.0488000	*
809.6525_8.065	rel_abund_log2	control	tomato	11	24	2.284442	15.98990	0.0363000	*
811.6703_8.961	rel_abund_log2	control	tomato	11	24	2.354898	24.37721	0.0269000	*
813.6854_10.226	rel_abund_log2	control	tomato	11	24	2.420896	25.44892	0.0229000	*
813.6866_9.902	rel_abund_log2	control	tomato	11	24	2.247674	31.67981	0.0317000	*
824.6527_9.167	rel_abund_log2	control	tomato	11	24	2.237067	24.65455	0.0346000	*
826.594_5.988	rel_abund_log2	control	tomato	11	24	-2.830945	18.73232	0.0108000	*
833.6507_8.971	rel_abund_log2	control	tomato	11	24	2.523234	20.54359	0.0200000	*
834.7053_6.506	rel_abund_log2	control	tomato	11	24	2.286380	17.25444	0.0351000	*
835.6664_10.22	rel_abund_log2	control	tomato	11	24	2.610768	29.56203	0.0140000	*
844.6529_8.109	rel_abund_log2	control	tomato	11	24	-2.446364	32.55766	0.0200000	*
859.6895_6.984	rel_abund_log2	control	tomato	11	24	-2.116553	30.10666	0.0427000	*
902.7673_8.957	rel_abund_log2	control	tomato	11	24	2.357429	21.43887	0.0280000	*
928.7831_8.964	rel_abund_log2	control	tomato	11	24	2.778580	27.08753	0.0098000	**

# how many are significant?
nrow(sig_tom_v_ctrl_t.test)

## [1] 78

ANOVA overlap

Keep sig features in t-test that have a match in sig ANOVA

sig_overlap_tom_ctrl <- inner_join(sig_tom_v_ctrl_t.test,
                                   trt_tukeyHSD_sig,
                                   by = "mz_rt",
                                   suffix = c(".t-test", ".tukeys"))

# which features overlap?
unique(sig_overlap_tom_ctrl$mz_rt)

##  [1] "1192.8412_6.709"  "1193.3431_6.71"   "1226.4736_8.96"   "1242.5051_10.22" 
##  [5] "1548.6249_6.858"  "1557.1328_6.71"   "1618.2939_8.956"  "1619.2973_8.957" 
##  [9] "162.1126_0.545"   "1624.3414_10.412" "1624.8418_9.901"  "1635.8345_9.901" 
## [13] "536.437_10.971"   "536.437_11.201"   "593.4762_4.644"   "774.5404_8.982"  
## [17] "781.62_7.043"     "785.6542_8.953"   "797.5914_8.07"    "799.6692_9.585"  
## [21] "801.686_10.409"   "809.6525_8.065"   "811.6703_8.961"   "813.6854_10.226" 
## [25] "813.6866_9.902"   "833.6507_8.971"   "835.6664_10.22"   "902.7673_8.957"  
## [29] "928.7831_8.964"

Heatmaps

ANOVA

# go back to wide for stats df
df_for_stats_wide <- df_for_stats %>%
  pivot_wider(names_from = mz_rt,
              values_from = rel_abund_log2)

ANOVA_trtperiod_heatmap_data <- df_for_stats_wide %>%
  filter(period == "b3") %>%
  dplyr::select(sample, 
                all_of(trt_anova_sig$mz_rt)) %>%
  column_to_rownames("sample")

head(ANOVA_trtperiod_heatmap_data, n=3)

##                         118.0864_0.597 1341.0272_6.83 1363.015_6.835
## x5107_b3_beta_c18pos_28       18.32788       10.56867       11.43975
## x5109_b3_beta_c18pos_81       18.08614       10.41244       12.33854
## x5112_b3_beta_c18pos_56       18.08859       11.01556       11.90621
##                         1365.0287_6.836 1531.6527_7.711 1585.2008_8.971
## x5107_b3_beta_c18pos_28        12.05681        13.46108        13.04438
## x5109_b3_beta_c18pos_81        12.47198        12.59145        12.83533
## x5112_b3_beta_c18pos_56        11.93622        12.77310        12.86113
##                         1586.2043_8.972 286.1437_1.13 308.1855_0.633
## x5107_b3_beta_c18pos_28        13.26054      15.59530       12.60870
## x5109_b3_beta_c18pos_81        12.85651      16.47807       13.91225
## x5112_b3_beta_c18pos_56        13.09636      15.03361       13.12646
##                         312.1597_1.413 373.2483_1.147 464.1914_0.656
## x5107_b3_beta_c18pos_28       13.99762       13.78420       12.43741
## x5109_b3_beta_c18pos_81       13.61853       14.30685       13.92181
## x5112_b3_beta_c18pos_56       13.47646       13.18580       12.64051
##                         526.2644_1.714 536.437_10.971 536.437_11.201
## x5107_b3_beta_c18pos_28       15.38726       14.13626       15.76905
## x5109_b3_beta_c18pos_81       15.60151       13.64178       15.56396
## x5112_b3_beta_c18pos_56       15.45049       14.33177       15.82453
##                         593.4757_4.54 593.4762_4.644 593.4768_4.312
## x5107_b3_beta_c18pos_28      11.81759       12.06375       11.55068
## x5109_b3_beta_c18pos_81      11.72187       11.96809       11.87066
## x5112_b3_beta_c18pos_56      11.77517       11.61433       11.76313
##                         596.4154_4.906 605.5497_9.629 610.431_5.16
## x5107_b3_beta_c18pos_28       13.59447       13.56674     14.05365
## x5109_b3_beta_c18pos_81       13.80423       12.90176     13.80027
## x5112_b3_beta_c18pos_56       13.21160       13.04551     13.89478
##                         650.6449_10.83 691.5742_6.403 692.6335_11.899
## x5107_b3_beta_c18pos_28       17.57799       12.29287        15.66660
## x5109_b3_beta_c18pos_81       17.31465       12.13172        15.67945
## x5112_b3_beta_c18pos_56       17.52072       12.27323        16.44264
##                         693.6368_11.899 719.6006_7.392 738.543_8.181
## x5107_b3_beta_c18pos_28        14.82319       12.66114      13.76122
## x5109_b3_beta_c18pos_81        14.81199       12.15745      12.91039
## x5112_b3_beta_c18pos_56        15.45162       12.72659      13.40845
##                         743.6054_7.227 760.5883_7.719 761.3681_7.718
## x5107_b3_beta_c18pos_28       13.93291       22.20178       12.80861
## x5109_b3_beta_c18pos_81       13.72383       21.82823       11.48494
## x5112_b3_beta_c18pos_56       13.47557       21.99390       12.37175
##                         761.4545_7.719 773.6516_9.025 775.6661_9.814
## x5107_b3_beta_c18pos_28       13.03432       12.89621       12.33325
## x5109_b3_beta_c18pos_81       12.09432       12.85221       12.39933
## x5112_b3_beta_c18pos_56       11.75753       13.11036       12.78094
##                         781.62_7.043 784.6654_10.38 787.6674_9.202
## x5107_b3_beta_c18pos_28     12.91163       15.55655       14.21447
## x5109_b3_beta_c18pos_81     12.93878       15.10685       13.97722
## x5112_b3_beta_c18pos_56     13.25657       15.17120       14.47030
##                         798.6518_8.391 807.6349_8.948 809.6525_8.065
## x5107_b3_beta_c18pos_28       14.13292       16.29715       15.81183
## x5109_b3_beta_c18pos_81       13.87908       16.11296       15.72594
## x5112_b3_beta_c18pos_56       14.14549       15.93857       15.72482
##                         810.6564_8.03 813.6834_9.437 825.6815_9.335
## x5107_b3_beta_c18pos_28      15.16108       13.53442       13.16572
## x5109_b3_beta_c18pos_81      15.19539       13.41267       13.13477
## x5112_b3_beta_c18pos_56      15.23552       14.05136       13.62055
##                         825.682_9.561 827.6998_10.277 827.6999_10.389
## x5107_b3_beta_c18pos_28      13.41194        16.75550        16.32508
## x5109_b3_beta_c18pos_81      13.17990        16.42048        15.16306
## x5112_b3_beta_c18pos_56      13.48333        16.99047        15.81504
##                         828.702_10.497 831.6353_8.196 856.5819_7.24 859.53_8.35
## x5107_b3_beta_c18pos_28       14.89963       12.80072      12.88760    12.14271
## x5109_b3_beta_c18pos_81       14.39998       12.92834      12.93171    11.76759
## x5112_b3_beta_c18pos_56       14.89397       13.25294      12.93838    11.31550
##                         865.5795_9.626 876.5695_7.88 878.7033_7.717
## x5107_b3_beta_c18pos_28       13.42176      12.81246       14.79617
## x5109_b3_beta_c18pos_81       12.52009      12.23240       14.61901
## x5112_b3_beta_c18pos_56       12.61819      12.72047       14.60653
##                         899.7091_11.203 912.8727_11.58
## x5107_b3_beta_c18pos_28        14.00587       12.14743
## x5109_b3_beta_c18pos_81        14.69047       12.11961
## x5112_b3_beta_c18pos_56        15.20439       12.09212

# pull metadata
metadata_Heatmap <- metadata

# change treatment to factor
metadata_Heatmap$treatment <- as.factor(metadata_Heatmap$treatment)

# make it so that rownames in metadata match rownames from heatmap df
rownames(metadata_Heatmap) <- rownames(ANOVA_trtperiod_heatmap_data)

# create annotation rows for treatment and wrangle
# select rownames (samples) from heatmap metadata (also ensures the order is correct)
anno_trt_row <- as.data.frame(rownames(metadata_Heatmap))

# pull trt column
anno_trt_row$treatment <- metadata_Heatmap$treatment
anno_trt_row$sex <- metadata_Heatmap$sex

# select trt
anno_trt_row <- anno_trt_row %>%
  dplyr::select(treatment, sex)

# get rownames to match heatmap again
rownames(anno_trt_row) <- rownames(metadata_Heatmap)

# create annotation colors
annotation_colors <- list(treatment = c("beta" = "orange",
                                        "control" = "green",
                                        "red" = "tomato"),
                          sex = c("M" = "burlywood",
                                  "F" = "pink"))

pheatmap(t(ANOVA_trtperiod_heatmap_data),
           scale = "row",
           cluster_rows = TRUE,
           annotation_col = anno_trt_row,
           annotation_colors = annotation_colors,
           cluster_cols = TRUE,
           show_colnames = TRUE,
           clustering_distance_rows = "euclidean",
           clustering_distance_cols = "euclidean",
           cutree_rows = 8,
           cutree_cols = 3,
           clustering_method = "ward.D2",
           color = colorRampPalette(c("#67a9cf", "#f7f7f7", "#ef8a62"))(16),
           main = "Heatmap of features significant in ANOVA across all treatment groups \nby Benjamoni-Hochberg corrected \np-values > 0.05 \nLipidomics C18 (+) \nShowing only post-intervention timepoints") 

# Without hierarchical clustering of samples (cols)
pheatmap(t(ANOVA_trtperiod_heatmap_data),
           scale = "row",
           cluster_rows = TRUE,
           annotation_col = anno_trt_row,
           annotation_colors = annotation_colors,
           cluster_cols = FALSE,
           show_colnames = TRUE,
           clustering_distance_rows = "euclidean",
           clustering_distance_cols = "euclidean",
           cutree_rows = 8,
           cutree_cols = 3,
           clustering_method = "ward.D2",
           color = colorRampPalette(c("#67a9cf", "#f7f7f7", "#ef8a62"))(16),
           main = "Heatmap of features significant in ANOVA across treatment groups \nby Benjamoni-Hochberg corrected \np-values > 0.05 \nLipidomics C18 (+) \nShowing only post-intervention timepoints") 

Unpaired

unpaired_t.tests_heatmap_data <- df_for_stats_wide %>%
  filter(period == "b3") %>%
  dplyr::select(sample, 
                all_of(sig_overlap_ctrl_beta$mz_rt),
                all_of(sig_overlap_ctrl_red$mz_rt),
                all_of(sig_overlap_tom_ctrl$mz_rt),
                all_of(sig_overlap_beta_red$mz_rt)) %>%
  column_to_rownames("sample")

head(unpaired_t.tests_heatmap_data,n = 3)

##                         118.0864_0.597 1192.8412_6.709 1193.3431_6.71
## x5107_b3_beta_c18pos_28       18.32788        12.08344       12.67894
## x5109_b3_beta_c18pos_81       18.08614        12.38855       12.49767
## x5112_b3_beta_c18pos_56       18.08859        11.98702       12.65512
##                         1226.4736_8.96 1242.5051_10.22 1242.5053_9.906
## x5107_b3_beta_c18pos_28       11.69151        11.37740        12.56389
## x5109_b3_beta_c18pos_81       11.00887        10.85121        11.97213
## x5112_b3_beta_c18pos_56       11.89162        11.32887        12.63237
##                         1548.6249_6.858 1557.1328_6.71 1558.1357_6.713
## x5107_b3_beta_c18pos_28        12.36216       12.16220        12.32980
## x5109_b3_beta_c18pos_81        11.95849       12.28881        12.53556
## x5112_b3_beta_c18pos_56        12.66611       11.93961        12.36858
##                         1597.1201_6.716 1618.2939_8.956 1619.2973_8.957
## x5107_b3_beta_c18pos_28        12.14798        11.65769        11.70411
## x5109_b3_beta_c18pos_81        11.78014        10.91371        10.89563
## x5112_b3_beta_c18pos_56        11.98181        11.25733        11.47127
##                         1624.3414_10.412 1624.8418_9.901 1635.8345_9.901
## x5107_b3_beta_c18pos_28         12.01037        12.39008        12.24433
## x5109_b3_beta_c18pos_81         11.48720        11.41456        11.10514
## x5112_b3_beta_c18pos_56         11.95295        11.91574        12.08204
##                         536.437_10.971 536.437_11.201 593.4762_4.644
## x5107_b3_beta_c18pos_28       14.13626       15.76905       12.06375
## x5109_b3_beta_c18pos_81       13.64178       15.56396       11.96809
## x5112_b3_beta_c18pos_56       14.33177       15.82453       11.61433
##                         692.5581_7.191 742.5718_8.28 774.5404_8.982
## x5107_b3_beta_c18pos_28       12.69678      13.67331       15.76036
## x5109_b3_beta_c18pos_81       12.95266      13.50327       15.53734
## x5112_b3_beta_c18pos_56       13.21042      13.77782       15.63822
##                         781.62_7.043 785.6542_8.953 796.5222_9.007
## x5107_b3_beta_c18pos_28     12.91163       19.29489       13.00877
## x5109_b3_beta_c18pos_81     12.93878       18.54979       13.00010
## x5112_b3_beta_c18pos_56     13.25657       18.78619       12.89030
##                         797.5914_8.07 799.6692_9.585 801.686_10.409
## x5107_b3_beta_c18pos_28      14.20812       17.69257       20.06030
## x5109_b3_beta_c18pos_81      14.18611       17.08662       19.54080
## x5112_b3_beta_c18pos_56      14.30850       17.43607       19.99056
##                         807.6349_8.948 809.6525_8.065 810.6564_8.03
## x5107_b3_beta_c18pos_28       16.29715       15.81183      15.16108
## x5109_b3_beta_c18pos_81       16.11296       15.72594      15.19539
## x5112_b3_beta_c18pos_56       15.93857       15.72482      15.23552
##                         811.6703_8.961 813.6854_10.226 827.6999_10.502
## x5107_b3_beta_c18pos_28       20.12794        19.05654        15.71804
## x5109_b3_beta_c18pos_81       19.20709        18.24428        15.20246
## x5112_b3_beta_c18pos_56       19.82178        18.58425        15.53037
##                         833.6507_8.971 835.6566_8.968 835.6664_10.22
## x5107_b3_beta_c18pos_28       16.88208       14.36366       16.87449
## x5109_b3_beta_c18pos_81       16.77536       14.08959       16.26508
## x5112_b3_beta_c18pos_56       17.03756       14.35579       16.51209
##                         902.7673_8.957 928.7831_8.964 1594.0976_6.869
## x5107_b3_beta_c18pos_28       12.56672       13.04733        12.09862
## x5109_b3_beta_c18pos_81       11.83950       12.61397        11.83166
## x5112_b3_beta_c18pos_56       12.12956       12.82448        11.78884
##                         162.1126_0.545 813.6866_9.902 1157.3434_6.852
## x5107_b3_beta_c18pos_28       17.23696       20.65068        12.79860
## x5109_b3_beta_c18pos_81       17.10793       19.67281        12.84831
## x5112_b3_beta_c18pos_56       17.04075       20.58214        13.12909
##                         1363.015_6.835 1511.145_6.854 1536.6252_6.856
## x5107_b3_beta_c18pos_28       11.43975       12.41331        12.11997
## x5109_b3_beta_c18pos_81       12.33854       11.73058        11.81181
## x5112_b3_beta_c18pos_56       11.90621       12.24789        12.34566
##                         699.5412_5.971 717.5903_7.018 805.6187_7.848
## x5107_b3_beta_c18pos_28       12.15782       15.52020       13.66922
## x5109_b3_beta_c18pos_81       11.89810       15.39155       13.45917
## x5112_b3_beta_c18pos_56       12.46739       15.37404       13.84118
##                         825.6815_9.335 834.5933_5.658
## x5107_b3_beta_c18pos_28       13.16572       12.83982
## x5109_b3_beta_c18pos_81       13.13477       13.01383
## x5112_b3_beta_c18pos_56       13.62055       12.85898

pheatmap(t(unpaired_t.tests_heatmap_data),
           scale = "row",
           cluster_rows = TRUE,
           annotation_col = anno_trt_row,
           annotation_colors = annotation_colors,
           cluster_cols = TRUE,
           show_colnames = TRUE,
           clustering_distance_rows = "euclidean",
           clustering_distance_cols = "euclidean",
         cutree_rows = 8,
         cutree_cols = 3,
           clustering_method = "ward.D2",
           color = colorRampPalette(c("#67a9cf", "#f7f7f7", "#ef8a62"))(16),
           main = "Heatmap of features significant in all unpaired t-test comparisons that overlap with sig ANOVA features across all treatment groups \nby Benjamoni-Hochberg corrected \np-values > 0.05 \nLipidomics C18 (+)")

# without clustering of cols
pheatmap(t(unpaired_t.tests_heatmap_data),
           scale = "row",
           cluster_rows = TRUE,
           annotation_col = anno_trt_row,
           annotation_colors = annotation_colors,
           cluster_cols = FALSE,
           show_colnames = TRUE,
           clustering_distance_rows = "euclidean",
           clustering_distance_cols = "euclidean",
         cutree_rows = 8,
         cutree_cols = 3,
           clustering_method = "ward.D2",
           color = colorRampPalette(c("#67a9cf", "#f7f7f7", "#ef8a62"))(16),
           main = "Heatmap of features significant in all unpaired t-test comparisons that overlap with sig ANOVA features across all treatment groups \nby Benjamoni-Hochberg corrected \np-values > 0.05 \nLipidomics C18 (+)")

Paired

Red

red_paired_t.tests_heatmap_data <- df_for_stats_wide %>%
  filter(treatment == "red") %>%
  dplyr::select(sample, period, treatment, sex,
                all_of(red_sig_ANOVA_overlap_paired$mz_rt)) %>%
  mutate_at("period", as.factor) %>%
  column_to_rownames("sample")

# create annotation rows for pre/post interventions and wrangle
# select rownames (samples) from heatmap metadata (also ensures the order is correct)
anno_red_row_paired <- as.data.frame(rownames(red_paired_t.tests_heatmap_data))


# pull period into a column
anno_red_row_paired$period <- red_paired_t.tests_heatmap_data$period
anno_red_row_paired$sex <- red_paired_t.tests_heatmap_data$sex

# select cols
anno_red_row_paired <- anno_red_row_paired %>%
  dplyr::select(period, sex)

# get rownames to match heatmap again
rownames(anno_red_row_paired) <- rownames(red_paired_t.tests_heatmap_data)

# create annotation colors
red_annotation_colors <- list(period = c("b1" = "darksalmon",
                                        "b3" = "red"),
                              sex = c("M" = "aquamarine2",
                                      "F" = "pink"))

pheatmap(t(red_paired_t.tests_heatmap_data[,-c(1:3)]),
           scale = "row",
           cluster_rows = TRUE,
           annotation_col = anno_red_row_paired,
           annotation_colors = red_annotation_colors,
           cluster_cols = TRUE,
           show_rownames = TRUE,
           clustering_distance_rows = "euclidean",
           clustering_distance_cols = "euclidean",
           cutree_rows = 8,
         cutree_cols = 2,
           clustering_method = "ward.D2",
           color = colorRampPalette(c("#67a9cf", "#f7f7f7", "#ef8a62"))(16),
           main = "Heatmap of significant features pre- vs. post-High-Lyc paired t-test \nsig in ANOVA across all treatment groups \nby Benjamoni-Hochberg corrected p-values > 0.05 \nBackground diet effect removed \nLipidomics C18 (+)")

Beta

beta_paired_t.tests_heatmap_data <- df_for_stats_wide %>%
  filter(treatment == "beta") %>%
  dplyr::select(sample, period, treatment, sex,
                all_of(beta_sig_ANOVA_overlap_paired$mz_rt)) %>%
  mutate_at("period", as.factor) %>%
  column_to_rownames("sample")

# create annotation rows for pre/post interventions and wrangle
# select rownames (samples) from heatmap metadata (also ensures the order is correct)
anno_beta_row_paired <- as.data.frame(rownames(beta_paired_t.tests_heatmap_data))


# pull period into a column
anno_beta_row_paired$period <- beta_paired_t.tests_heatmap_data$period
anno_beta_row_paired$sex <- beta_paired_t.tests_heatmap_data$sex

# select cols
anno_beta_row_paired <- anno_beta_row_paired %>%
  dplyr::select(sex, period)

# get rownames to match heatmap again
rownames(anno_beta_row_paired) <- rownames(beta_paired_t.tests_heatmap_data)

# create annotation colors
beta_annotation_colors <- list(period = c("b1" = "bisque",
                                        "b3" = "darkorange"),
                              sex = c("M" = "aquamarine2",
                                      "F" = "pink"))

pheatmap(t(beta_paired_t.tests_heatmap_data[,-c(1:3)]),
           scale = "row",
           cluster_rows = TRUE,
           annotation_col = anno_beta_row_paired,
           annotation_colors = beta_annotation_colors,
           cluster_cols = TRUE,
           show_rownames = TRUE,
           clustering_distance_rows = "euclidean",
           clustering_distance_cols = "euclidean",
           cutree_rows = 10,
         cutree_cols = 5,
           clustering_method = "ward.D2",
           color = colorRampPalette(c("#67a9cf", "#f7f7f7", "#ef8a62"))(16),
           main = "Heatmap of significant features pre- vs. post-High-beta paired t-test \nsig in ANOVA across all treatment groups \nby Benjamoni-Hochberg corrected p-values > 0.05 \nBackground diet effect removed \nLipidomics C18 (+)")

Tomato

tom_paired_t.tests_heatmap_data <- df_for_stats_wide %>%
  filter(treatment != "control") %>%
  dplyr::select(sample, period, tomato_or_control, sex,
                all_of(tom_sig_ANOVA_overlap_paired$mz_rt)) %>%
  mutate_at("period", as.factor) %>%
  column_to_rownames("sample")

# create annotation rows for pre/post interventions and wrangle
# select rownames (samples) from heatmap metadata (also ensures the order is correct)
anno_tom_row_paired <- as.data.frame(rownames(tom_paired_t.tests_heatmap_data))


# pull period into a column
anno_tom_row_paired$period <- tom_paired_t.tests_heatmap_data$period
anno_tom_row_paired$sex <- tom_paired_t.tests_heatmap_data$sex

# select cols
anno_tom_row_paired <- anno_tom_row_paired %>%
  dplyr::select(period, sex)

# get rownames to match heatmap again
rownames(anno_tom_row_paired) <- rownames(tom_paired_t.tests_heatmap_data)

# create annotation colors
tom_annotation_colors <- list(period = c("b1" = "darksalmon",
                                        "b3" = "tomato"),
                              sex = c("M" = "aquamarine2",
                                      "F" = "pink"))

pheatmap(t(tom_paired_t.tests_heatmap_data[,-c(1:3)]),
           scale = "row",
           cluster_rows = TRUE,
           annotation_col = anno_tom_row_paired,
           annotation_colors = tom_annotation_colors,
           cluster_cols = TRUE,
           show_rownames = TRUE,
           clustering_distance_rows = "euclidean",
           clustering_distance_cols = "euclidean",
           cutree_rows = 8,
         cutree_cols = 2,
           clustering_method = "ward.D2",
           color = colorRampPalette(c("#67a9cf", "#f7f7f7", "#ef8a62"))(16),
           main = "Heatmap of significant features pre- vs. post-Tomato paired t-test \nsig in ANOVA across all treatment groups \nby Benjamoni-Hochberg corrected p-values > 0.05 \nBackground diet effect removed \nLipidomics C18 (+)")

Joined lists

df_for_stats <- df_for_stats %>%
  # add rel abund levels back since this got lost during drift correction
  mutate(rel_abund = 2^(rel_abund_log2))

Using t tests sig

Tomato effects

How many features are significant (with background diet effect removed)

dim(sig_paired_tom_rmBG)

## [1] 460  20

Which features are significant in unpaired t test comparisons?

# select only significant features from pre v post tomato effect that have a matching key to significant features post tomato v. post control comparison

overall_tomato_effect <- semi_join(sig_paired_tom_rmBG,
                                   sig_tom_v_ctrl_t.test,
                                  by = "mz_rt")

dim(overall_tomato_effect)

## [1] 14 20

Summary stats

(FC_tomato_effect <- df_for_stats %>%
  filter(mz_rt %in% overall_tomato_effect$mz_rt) %>%
  select(subject, treatment_period, mz_rt, rel_abund) %>%
  group_by(mz_rt) %>%
  pivot_wider(names_from = treatment_period,
              values_from = rel_abund) %>%
  mutate(control_FC = control_b3/control_b1,
         beta_FC = beta_b3/beta_b1,
         red_FC = red_b3/red_b1) %>%
  summarize(mean_control_FC = mean(control_FC, na.rm = TRUE),
            mean_beta_FC = mean(beta_FC, na.rm = TRUE),
            mean_red_FC = mean(red_FC, na.rm = TRUE),
            mean_ctrl_beta_FC = mean(mean(beta_FC, na.rm = TRUE)/mean(control_FC, na.rm = TRUE)),
            mean_ctrl_red_FC = mean(mean(red_FC, na.rm = TRUE)/mean(control_FC, na.rm = TRUE)),
            mean_red_beta_FC = mean(mean(beta_FC, na.rm = TRUE)/mean(red_FC, na.rm = TRUE))))

## # A tibble: 14 × 7
##    mz_rt           mean_control_FC mean_beta_FC mean_red_FC mean_ctrl_beta_FC
##    <chr>                     <dbl>        <dbl>       <dbl>             <dbl>
##  1 1195.8336_6.857           1.01         1.05        1.05              1.04 
##  2 1585.1303_6.711           1.24         1.13        1.26              0.913
##  3 1596.1232_6.714           1.23         1.23        1.22              1.00 
##  4 1611.8348_9.901           0.976        0.878       0.981             0.900
##  5 1619.2973_8.957           0.972        0.874       0.909             0.900
##  6 1624.8418_9.901           1.21         1.22        1.25              1.01 
##  7 1635.8345_9.901           1.11         1.17        1.16              1.05 
##  8 1636.3352_9.901           1.25         1.22        1.10              0.978
##  9 536.437_11.201            0.806        2.62        0.893             3.25 
## 10 704.3938_6.501            1.05         0.944       0.896             0.897
## 11 744.5538_6.51             1.09         1.16        1.08              1.07 
## 12 774.5404_8.982            1.14         1.20        1.17              1.05 
## 13 794.6868_10.627           1.02         0.937       0.925             0.920
## 14 813.6866_9.902            1.13         1.19        1.16              1.05 
## # ℹ 2 more variables: mean_ctrl_red_FC <dbl>, mean_red_beta_FC <dbl>

Compile list

# combine cluster and FC info
tom_effect_list <- left_join(FC_tomato_effect, 
                             cluster_features,
                             by = "mz_rt") %>%
  select(mz_rt, mz, rt, Cluster_ID, Cluster_features, Cluster_size, everything())

# add in averages for each group + timepoint
tom_effect_list <-
  left_join(tom_effect_list,
            (df_for_stats %>%
               group_by(treatment_period, mz_rt) %>%
               summarize(mean_rel_abund = mean(rel_abund)) %>%
               pivot_wider(names_from = treatment_period, values_from = mean_rel_abund)),
            by = "mz_rt")

head(tom_effect_list, n=1)

## # A tibble: 1 × 19
##   mz_rt        mz    rt Cluster_ID Cluster_features Cluster_size mean_control_FC
##   <chr>     <dbl> <dbl> <chr>      <chr>                   <dbl>           <dbl>
## 1 1195.833… 1196.  6.86 1195.8336… 1195.8336_6.857             1            1.01
## # ℹ 12 more variables: mean_beta_FC <dbl>, mean_red_FC <dbl>,
## #   mean_ctrl_beta_FC <dbl>, mean_ctrl_red_FC <dbl>, mean_red_beta_FC <dbl>,
## #   MPA <dbl>, beta_b1 <dbl>, beta_b3 <dbl>, control_b1 <dbl>,
## #   control_b3 <dbl>, red_b1 <dbl>, red_b3 <dbl>

Export list

write_csv(tom_effect_list, "effect lists/tomato-effect.csv")

Boxplots

# make comparison list for comparison tests
my_comparisons <- list( c("beta_b1", "beta_b3"), 
                        c("red_b1", "red_b3"),
                        c("control_b1", "control_b3") )

# subset df for uniquely significant features
justT_tom_effect_df <- df_for_stats_wide %>%
  dplyr::select(c(1:21),
                (all_of(overall_tomato_effect$mz_rt)))

# make tidy df
justT_tom_effect_df_tidy <- justT_tom_effect_df %>%
  pivot_longer(cols = 22:ncol(.),
               names_to = "mz_rt",
               values_to = "rel_abund_log2")

# fix factor levels for time points
justT_tom_effect_df_tidy$treatment_period <- factor(justT_tom_effect_df_tidy$treatment_period,
                              levels = c("control_b1", "control_b3", 
                                         "beta_b1", "beta_b3",
                                         "red_b1", "red_b3"))

# check
levels(justT_tom_effect_df_tidy$treatment_period)  

## [1] "control_b1" "control_b3" "beta_b1"    "beta_b3"    "red_b1"    
## [6] "red_b3"

justT_tom_effect_df_tidy$treatment <- factor(justT_tom_effect_df_tidy$treatment,
                              levels = c("control", "beta", "red"))

justT_tom_effect_df_tidy %>% 
  ggplot(aes(x = treatment_period, y = rel_abund_log2, fill = treatment_period)) +
  geom_boxplot() +
  scale_fill_manual(values = c("darkseagreen2", "darkgreen", 
                               "tan", "orangered2",
                               "lavenderblush3", "darkred"),
                    labels = c("pre control", "post control",
                               "pre beta", "post beta",
                               "pre lyc", "post lyc")) +
  scale_x_discrete(labels = c("", "", "", "", "", "")) +
  geom_line(aes(group = subject, colour = subject), size = 0.2) +
  theme_classic(base_size = 12, base_family = "sans") +
  facet_wrap(vars(mz_rt), scales = "free_y", ncol = 4) + 
  stat_compare_means(comparisons = my_comparisons, method = "t.test", paired = TRUE, p.adjust.method = "BH") +
  labs(x = "",
       y = "Log2 transformed relative abundance",
       title = "Tomato effect - significant when compared to control, also significant pre and post tomato",
       subtitle = "FDR adj. p-values from T-tests")

Beta effects

dim(sig_paired_beta_rmBG)

## [1] 342  20

Which features are significant in paired t test comparisons for beta, but not significant in the same direction for control?

Features that are significant in both pre- vs. post. beta AND post-beta vs. post-control

# select features that are only significant post beta v. post control comparison
overall_beta_effect <- semi_join(sig_paired_beta_rmBG,
                                 sig_beta_v_ctrl_t.test,
                                 by = "mz_rt")
dim(overall_beta_effect)

## [1]  8 20

overall_unique_beta_effect <- left_join(overall_beta_effect,
                                        sig_beta_v_red_t.test,
                                        by = "mz_rt")
dim(overall_unique_beta_effect)

## [1]  8 29

Summary stats

(FC_beta_effect <- df_for_stats %>%
  filter(mz_rt %in% overall_beta_effect$mz_rt) %>%
  select(subject, treatment_period, mz_rt, rel_abund) %>%
  group_by(mz_rt) %>%
  pivot_wider(names_from = treatment_period,
              values_from = rel_abund) %>%
  mutate(control_FC = control_b3/control_b1,
         beta_FC = beta_b3/beta_b1,
         red_FC = red_b3/red_b1) %>%
  summarize(mean_control_FC = mean(control_FC, na.rm = TRUE),
            mean_beta_FC = mean(beta_FC, na.rm = TRUE),
            mean_red_FC = mean(red_FC, na.rm = TRUE),
            mean_ctrl_beta_FC = mean(mean(beta_FC, na.rm = TRUE)/mean(control_FC, na.rm = TRUE)),
            mean_ctrl_red_FC = mean(mean(red_FC, na.rm = TRUE)/mean(control_FC, na.rm = TRUE)),
            mean_red_beta_FC = mean(mean(beta_FC, na.rm = TRUE)/mean(red_FC, na.rm = TRUE))))

## # A tibble: 8 × 7
##   mz_rt           mean_control_FC mean_beta_FC mean_red_FC mean_ctrl_beta_FC
##   <chr>                     <dbl>        <dbl>       <dbl>             <dbl>
## 1 1002.658_3.079            0.988        1.10        0.971             1.11 
## 2 1611.8348_9.901           0.976        0.878       0.981             0.900
## 3 1618.2939_8.956           0.997        0.859       1.01              0.862
## 4 1619.2973_8.957           0.972        0.874       0.909             0.900
## 5 536.437_11.201            0.806        2.62        0.893             3.25 
## 6 774.5404_8.982            1.14         1.20        1.17              1.05 
## 7 796.5222_9.007            1.12         1.16        1.14              1.04 
## 8 835.6566_8.968            1.12         1.13        1.07              1.01 
## # ℹ 2 more variables: mean_ctrl_red_FC <dbl>, mean_red_beta_FC <dbl>

Compile list

# combine cluster and FC info
beta_effect_list <- left_join(FC_beta_effect, 
                              cluster_features,
                              by = "mz_rt") %>%
  select(mz_rt, mz, rt, Cluster_ID, Cluster_features, Cluster_size, everything())

# add in averages for each group + timepoint
beta_effect_list <-
  left_join(beta_effect_list,
            (df_for_stats %>%
               group_by(treatment_period, mz_rt) %>%
               summarize(mean_rel_abund = mean(rel_abund)) %>%
               pivot_wider(names_from = treatment_period, values_from = mean_rel_abund)),
            by = "mz_rt")

head(beta_effect_list, n=1)

## # A tibble: 1 × 19
##   mz_rt        mz    rt Cluster_ID Cluster_features Cluster_size mean_control_FC
##   <chr>     <dbl> <dbl> <chr>      <chr>                   <dbl>           <dbl>
## 1 1002.658… 1003.  3.08 1002.658_… 1002.658_3.079              1           0.988
## # ℹ 12 more variables: mean_beta_FC <dbl>, mean_red_FC <dbl>,
## #   mean_ctrl_beta_FC <dbl>, mean_ctrl_red_FC <dbl>, mean_red_beta_FC <dbl>,
## #   MPA <dbl>, beta_b1 <dbl>, beta_b3 <dbl>, control_b1 <dbl>,
## #   control_b3 <dbl>, red_b1 <dbl>, red_b3 <dbl>

Export list

write_csv(beta_effect_list, "effect lists/beta-effect.csv")

Boxplots

# subset df for uniquely significant features
justT_beta_effect_df <- df_for_stats_wide %>%
  dplyr::select(c(1:21),
                (all_of(overall_beta_effect$mz_rt)))

# make tidy df
justT_beta_effect_df_tidy <- justT_beta_effect_df %>%
  pivot_longer(cols = 22:ncol(.),
               names_to = "mz_rt",
               values_to = "rel_abund_log2")

# fix factor levels for time points
justT_beta_effect_df_tidy$treatment_period <- factor(justT_beta_effect_df_tidy$treatment_period,
                              levels = c("control_b1", "control_b3", 
                                         "beta_b1", "beta_b3",
                                         "red_b1", "red_b3"))

# check
levels(justT_beta_effect_df_tidy$treatment_period)  

## [1] "control_b1" "control_b3" "beta_b1"    "beta_b3"    "red_b1"    
## [6] "red_b3"

justT_beta_effect_df_tidy$treatment <- factor(justT_beta_effect_df_tidy$treatment,
                              levels = c("control", "beta", "red"))

justT_beta_effect_df_tidy$period <- factor(justT_beta_effect_df_tidy$period,
                              levels = c("b1", "b3"))

justT_beta_effect_df_tidy %>% 
  ggplot(aes(x = treatment_period, y = rel_abund_log2, fill = treatment_period)) +
  geom_boxplot() +
  scale_fill_manual(values = c("darkseagreen2", "darkgreen", 
                               "tan", "orangered2",
                               "lavenderblush3", "darkred"),
                    labels = c("pre control", "post control",
                               "pre beta", "post beta",
                               "pre lyc", "post lyc")) +
  scale_x_discrete(labels = c("", "", "", "", "", "")) +
  geom_line(aes(group = subject, colour = subject), size = 0.2) +
  theme_classic(base_size = 12, base_family = "sans") +
  facet_wrap(vars(mz_rt), scales = "free_y", nrow = 3) + 
  stat_compare_means(comparisons = my_comparisons, method = "t.test", paired = TRUE, p.adjust.method = "BH") +
  labs(x = "",
       y = "Log2 transformed relative abundance",
       title = "High beta juice effect - significant when compared to control, also significant pre and post beta juice",
       subtitle = "FDR adj. p-values from T-tests")

m/z 1002

(mz1002_bps <- justT_beta_effect_df_tidy %>% 
   filter(mz_rt == "1002.658_3.079") %>%
  ggpaired(x = "period", y = "rel_abund_log2", fill = "treatment", 
           line.color = "gray", 
           line.size = 1, 
           facet.by = "treatment", 
           short.panel.labs = FALSE, panel.labs = list(treatment = c("Control", "High \n\u03b2-Carotene", "High \nLycopene"))) +
  scale_fill_manual(values = c("lightgreen", 
                               "orange",
                               "red"),
                    labels = c("Control",
                               "High \u03b2-Carotene",
                               "High Lycopene"),
                    name = "Treatment") +
   scale_x_discrete(labels = c("b1" = "pre",
                               "b3" = "post")) +
  geom_line(aes(group = subject), colour = "gray", linewidth = 0.15) +
  theme_classic(base_size = 18, base_family = "sans") +
  labs(x = "",
       y = "Log2 relative abundance",
       title = "LPC 16:0 levels at each timepoint",
       subtitle = "m/z 1002.658, RT 3.079 min") +
   stat_compare_means(method = "t.test", paired = TRUE) +
   theme(legend.position = "none"))

ggsave("plots and figures/mz1002bps.svg", height = 6, width = 8)

beta-carotene

# for Gordon conference
(bC_bps <- justT_beta_effect_df_tidy %>% 
   filter(mz_rt == "536.437_11.201") %>%
  ggpaired(x = "period", y = "rel_abund_log2", fill = "treatment", 
           line.color = "gray", 
           line.size = 1, 
           facet.by = "treatment", 
           short.panel.labs = FALSE, panel.labs = list(treatment = c("Control", "High \n\u03b2-Carotene", "High \nLycopene"))) +
  scale_fill_manual(values = c("lightgreen", 
                               "orange",
                               "red"),
                    labels = c("Control",
                               "High \u03b2-Carotene",
                               "High Lycopene"),
                    name = "Treatment") +
   scale_x_discrete(labels = c("b1" = "pre",
                               "b3" = "post")) +
  geom_line(aes(group = subject), colour = "gray", linewidth = 0.15) +
  theme_classic(base_size = 18, base_family = "sans") +
  labs(x = "",
       y = "Log2 relative abundance",
       title = "\u03b2-Carotene levels at each timepoint") +
   stat_compare_means(method = "t.test", paired = TRUE) +
   theme(legend.position = "none"))

bC and 1002

bC_bps + mz1002_bps + plot_layout(axes = "collect")

ggsave("plots and figures/bCandLPC16-bps.svg", height = 6, width = 12)

1002 investigations

df_for_stats$period <- factor(df_for_stats$period,
                              levels = c("b1", "b3"))

df_for_stats %>% 
   filter(mz_rt == "496.342_3.082") %>%
  ggpaired(x = "period", y = "rel_abund_log2", fill = "treatment", 
           line.color = "gray", 
           line.size = 1, 
           facet.by = "treatment", 
           short.panel.labs = FALSE, panel.labs = list(treatment = c("Control", "High \n\u03b2-Carotene", "High \nLycopene"))) +
  scale_fill_manual(values = c("lightgreen", 
                               "orange",
                               "red"),
                    labels = c("Control",
                               "High \u03b2-Carotene",
                               "High Lycopene"),
                    name = "Treatment") +
   scale_x_discrete(labels = c("b1" = "pre",
                               "b3" = "post")) +
  geom_line(aes(group = subject), colour = "gray", linewidth = 0.15) +
  theme_classic(base_size = 18, base_family = "sans") +
  labs(x = "",
       y = "Log2 relative abundance",
       title = "    
496.342_3.082 levels at each timepoint") +
   stat_compare_means(method = "t.test", paired = TRUE) +
   theme(legend.position = "none")

I am plotting this mass because I believe it is LPC 16:0, which is what I believe m/z 1002 is an adduct for. These levels (for m/z 496) are unchanging, but when you look at the chromatograms, this mass is oversaturated and may be outside of range of linearity, possibly the reason why this mass doesn’t seem to change

Next, we’ll look at 991 which is likely the the dimer form of m/z 496

991.6743_3.082

df_for_stats %>% 
   filter(mz_rt == "991.6743_3.082") %>%
  ggpaired(x = "period", y = "rel_abund_log2", fill = "treatment", 
           line.color = "gray", 
           line.size = 1, 
           facet.by = "treatment", 
           short.panel.labs = FALSE, panel.labs = list(treatment = c("Control", "High \n\u03b2-Carotene", "High \nLycopene"))) +
  scale_fill_manual(values = c("lightgreen", 
                               "orange",
                               "red"),
                    labels = c("Control",
                               "High \u03b2-Carotene",
                               "High Lycopene"),
                    name = "Treatment") +
   scale_x_discrete(labels = c("b1" = "pre",
                               "b3" = "post")) +
  geom_line(aes(group = subject), colour = "gray", linewidth = 0.15) +
  theme_classic(base_size = 18, base_family = "sans") +
  labs(x = "",
       y = "Log2 relative abundance",
       title = "    
991.6743_3.082 levels at each timepoint") +
   stat_compare_means(method = "t.test", paired = TRUE) +
   theme(legend.position = "none")

df_for_stats %>% 
   filter(mz_rt == "498.346_3.082") %>%
  ggpaired(x = "period", y = "rel_abund_log2", fill = "treatment", 
           line.color = "gray", 
           line.size = 1, 
           facet.by = "treatment", 
           short.panel.labs = FALSE, panel.labs = list(treatment = c("Control", "High \n\u03b2-Carotene", "High \nLycopene"))) +
  scale_fill_manual(values = c("lightgreen", 
                               "orange",
                               "red"),
                    labels = c("Control",
                               "High \u03b2-Carotene",
                               "High Lycopene"),
                    name = "Treatment") +
   scale_x_discrete(labels = c("b1" = "pre",
                               "b3" = "post")) +
  geom_line(aes(group = subject), colour = "gray", linewidth = 0.15) +
  theme_classic(base_size = 18, base_family = "sans") +
  labs(x = "",
       y = "Log2 relative abundance",
       title = "    
498.346_3.082 levels at each timepoint") +
   stat_compare_means(method = "t.test", paired = TRUE) +
   theme(legend.position = "none")

cor(x = df_for_stats_wide$`496.342_3.082`, y = df_for_stats_wide$`1002.658_3.079`)

## [1] 0.6446398

cor(x = df_for_stats_wide$`991.6743_3.082`, y = df_for_stats_wide$`1002.658_3.079`)

## [1] 0.6569136

cor(x = df_for_stats_wide$`991.6743_3.082`, y = df_for_stats_wide$`1002.658_3.079`)

## [1] 0.6569136

835 investigation

Here, I’m trying to see if m/z 835 is really the C14 isotopologue of m.z 833. Let’s look to see if trends are similar.

df_for_stats %>% 
   filter(mz_rt %in% c("833.6507_8.971", "835.6566_8.968")) %>%
  ggpaired(x = "period", y = "rel_abund_log2", fill = "treatment", 
           line.color = "gray", 
           line.size = 1, 
           facet.by = c("treatment", "mz_rt"), 
           short.panel.labs = FALSE, panel.labs = list(treatment = c("Control", "High \n\u03b2-Carotene", "High \nLycopene"))) +
  scale_fill_manual(values = c("lightgreen", 
                               "orange",
                               "red"),
                    labels = c("Control",
                               "High \u03b2-Carotene",
                               "High Lycopene"),
                    name = "Treatment") +
   scale_x_discrete(labels = c("b1" = "pre",
                               "b3" = "post")) +
  geom_line(aes(group = subject), colour = "gray", linewidth = 0.15) +
  theme_classic(base_size = 18, base_family = "sans") +
  labs(x = "",
       y = "Log2 relative abundance",
       title = "    
833.6507_8.971, 835.6566_8.968 levels at each timepoint") +
   stat_compare_means(method = "t.test", paired = TRUE) +
   theme(legend.position = "none")

cor(df_for_stats_wide$`833.6507_8.971`, df_for_stats_wide$`835.6566_8.968`)

## [1] 0.9359787

These definitely should have been clustered!

Red effects

dim(sig_paired_red_rmBG)

## [1] 195  20

# select features that are only significant post red v. post control comparison

overall_red_effect <- semi_join(sig_paired_red_rmBG,
                                 sig_red_v_ctrl_t.test,
                                  by = "mz_rt")
dim(overall_red_effect)

## [1]  2 20

# select features that are only significant post tomato v. post control comparison

overall_unique_red_effect <- left_join(overall_red_effect,
                                        sig_beta_v_red_t.test,
                                        by = "mz_rt")
dim(overall_unique_red_effect)

## [1]  2 29

head(overall_unique_red_effect)

## # A tibble: 2 × 29
##   mz_rt .y..red group1.red group2.red n1.red n2.red statistic.red df.red   p.red
##   <chr> <chr>   <chr>      <chr>       <int>  <int>         <dbl>  <dbl>   <dbl>
## 1 536.… rel_ab… b1         b3             12     12         -8.54     11 3.5 e-6
## 2 760.… rel_ab… b1         b3             12     12         -2.32     11 4.04e-2
## # ℹ 20 more variables: p.signif.red <chr>, .y..ctrl <chr>, group1.ctrl <chr>,
## #   group2.ctrl <chr>, n1.ctrl <int>, n2.ctrl <int>, statistic.ctrl <dbl>,
## #   df.ctrl <dbl>, p.ctrl <dbl>, p.signif.ctrl <chr>, sign <dbl>, .y. <chr>,
## #   group1 <chr>, group2 <chr>, n1 <int>, n2 <int>, statistic <dbl>, df <dbl>,
## #   p <dbl>, p.signif <chr>

Summary stats

(FC_red_effect <- df_for_stats %>%
  filter(mz_rt %in% overall_red_effect$mz_rt) %>%
  select(subject, treatment_period, mz_rt, rel_abund) %>%
  group_by(mz_rt) %>%
  pivot_wider(names_from = treatment_period,
              values_from = rel_abund) %>%
  mutate(control_FC = control_b3/control_b1,
         beta_FC = beta_b3/beta_b1,
         red_FC = red_b3/red_b1) %>%
  summarize(mean_control_FC = mean(control_FC, na.rm = TRUE),
            mean_beta_FC = mean(beta_FC, na.rm = TRUE),
            mean_red_FC = mean(red_FC, na.rm = TRUE),
            mean_ctrl_beta_FC = mean(mean(beta_FC, na.rm = TRUE)/mean(control_FC, na.rm = TRUE)),
            mean_ctrl_red_FC = mean(mean(red_FC, na.rm = TRUE)/mean(control_FC, na.rm = TRUE)),
            mean_red_beta_FC = mean(mean(beta_FC, na.rm = TRUE)/mean(red_FC, na.rm = TRUE))))

## # A tibble: 2 × 7
##   mz_rt          mean_control_FC mean_beta_FC mean_red_FC mean_ctrl_beta_FC
##   <chr>                    <dbl>        <dbl>       <dbl>             <dbl>
## 1 536.437_10.971           0.547        0.690        2.62              1.26
## 2 760.4925_6.98            0.980        0.994        1.05              1.01
## # ℹ 2 more variables: mean_ctrl_red_FC <dbl>, mean_red_beta_FC <dbl>

Compile list

# combine cluster and FC info
red_effect_list <- left_join(FC_red_effect, 
                             cluster_features,
                             by = "mz_rt") %>%
  select(mz_rt, mz, rt, Cluster_ID, Cluster_features, Cluster_size, everything())

# add in averages for each group + timepoint
red_effect_list <-
  left_join(red_effect_list,
            (df_for_stats %>%
               group_by(treatment_period, mz_rt) %>%
               summarize(mean_rel_abund = mean(rel_abund)) %>%
               pivot_wider(names_from = treatment_period, values_from = mean_rel_abund)),
            by = "mz_rt")

head(red_effect_list, n=1)

## # A tibble: 1 × 19
##   mz_rt        mz    rt Cluster_ID Cluster_features Cluster_size mean_control_FC
##   <chr>     <dbl> <dbl> <chr>      <chr>                   <dbl>           <dbl>
## 1 536.437_…  536.  11.0 Cluster_5… 536.437_10.971;…            2           0.547
## # ℹ 12 more variables: mean_beta_FC <dbl>, mean_red_FC <dbl>,
## #   mean_ctrl_beta_FC <dbl>, mean_ctrl_red_FC <dbl>, mean_red_beta_FC <dbl>,
## #   MPA <dbl>, beta_b1 <dbl>, beta_b3 <dbl>, control_b1 <dbl>,
## #   control_b3 <dbl>, red_b1 <dbl>, red_b3 <dbl>

Export list

write_csv(red_effect_list, "effect lists/red-effect.csv")

Boxplots

# subset df for uniquely significant features
justT_red_effect_df <- df_for_stats_wide %>%
  dplyr::select(c(1:21),
                (all_of(overall_unique_red_effect$mz_rt)))

# make tidy df
justT_red_effect_df_tidy <- justT_red_effect_df %>%
  pivot_longer(cols = 22:ncol(.),
               names_to = "mz_rt",
               values_to = "rel_abund_log2")

# fix factor levels for time points
justT_red_effect_df_tidy$treatment_period <- factor(justT_red_effect_df_tidy$treatment_period,
                              levels = c("control_b1", "control_b3", 
                                         "beta_b1", "beta_b3",
                                         "red_b1", "red_b3"))

# check
levels(justT_red_effect_df_tidy$treatment_period)  

## [1] "control_b1" "control_b3" "beta_b1"    "beta_b3"    "red_b1"    
## [6] "red_b3"

justT_red_effect_df_tidy$treatment <- factor(justT_red_effect_df_tidy$treatment,
                              levels = c("control", "beta", "red"))

levels(justT_red_effect_df_tidy$treatment)

## [1] "control" "beta"    "red"

justT_red_effect_df_tidy$period <- factor(justT_red_effect_df_tidy$period,
                              levels = c("b1", "b3"))

justT_red_effect_df_tidy %>% 
  ggplot(aes(x = treatment_period, y = rel_abund_log2, fill = treatment_period)) +
  geom_boxplot() +
  scale_fill_manual(values = c("darkseagreen2", "darkgreen", 
                               "tan", "orangered2",
                               "lavenderblush3", "darkred"),
                    labels = c("pre control", "post control",
                               "pre beta", "post beta",
                               "pre lyc", "post lyc")) +
  scale_x_discrete(labels = c("", "", "", "", "", "")) +
  geom_line(aes(group = subject, colour = subject), size = 0.2) +
  theme_classic(base_size = 12, base_family = "sans") +
  facet_wrap(vars(mz_rt), scales = "free_y") + 
  stat_compare_means(comparisons = my_comparisons, method = "t.test", paired = TRUE, p.adjust.method = "BH") +
  labs(x = "",
       y = "Log2 transformed relative abundance",
       title = "High lyc effect - significant when compared to control, \nsignificant pre vs post red",
       subtitle = "FDR adj. p-values from T-tests")

lyc

(lycbps <- justT_red_effect_df_tidy %>% 
   filter(mz_rt == "536.437_10.971") %>%
  ggpaired(x = "period", y = "rel_abund_log2", fill = "treatment", 
           line.color = "gray", 
           line.size = 1, 
           facet.by = "treatment", 
           short.panel.labs = FALSE, panel.labs = list(treatment = c("Control", "High \n\u03b2-Carotene", "High \nLycopene"))) +
  scale_fill_manual(values = c("lightgreen", 
                               "orange",
                               "red"),
                    labels = c("Control",
                               "High \u03b2-Carotene",
                               "High Lycopene"),
                    name = "Treatment") +
   scale_x_discrete(labels = c("b1" = "pre",
                               "b3" = "post")) +
  geom_line(aes(group = subject), colour = "gray", linewidth = 0.15) +
  theme_classic(base_size = 18, base_family = "sans") +
  labs(x = "",
       y = "Log2 relative abundance",
       title = "    
Lycopene levels at each timepoint") +
   stat_compare_means(method = "t.test", paired = TRUE) +
   theme(legend.position = "none"))

m/z760

(mz760bps <- justT_red_effect_df_tidy %>% 
   filter(mz_rt == "760.4925_6.98") %>%
  ggpaired(x = "period", y = "rel_abund_log2", fill = "treatment", 
           line.color = "gray", 
           line.size = 1, 
           facet.by = "treatment", 
           short.panel.labs = FALSE, panel.labs = list(treatment = c("Control", "High \n\u03b2-Carotene", "High \nLycopene"))) +
  scale_fill_manual(values = c("lightgreen", 
                               "orange",
                               "red"),
                    labels = c("Control",
                               "High \u03b2-Carotene",
                               "High Lycopene"),
                    name = "Treatment") +
   scale_x_discrete(labels = c("b1" = "pre",
                               "b3" = "post")) +
  geom_line(aes(group = subject), colour = "gray", linewidth = 0.15) +
  theme_classic(base_size = 18, base_family = "sans") +
  labs(x = "",
       y = "Log2 relative abundance",
       title = "PC levels at each timepoint",
       subtitle = "m/z 760.4925, RT 6.98 min.") +
   stat_compare_means(method = "t.test", paired = TRUE) +
   theme(legend.position = "none"))

lyc + m/z760

lycbps + mz760bps + plot_layout(axes = "collect")

ggsave("plots and figures/LYCandmz760-bps.svg", height = 8, width = 12)

760 investigation

This feature seems to be getting massed by another mass (760.5281). Let’s see if the levels are comparable

df_for_stats %>% 
   filter(mz_rt == "760.5281_6.984") %>%
  ggpaired(x = "period", y = "rel_abund_log2", fill = "treatment", 
           line.color = "gray", 
           line.size = 1, 
           facet.by = "treatment", 
           short.panel.labs = FALSE, panel.labs = list(treatment = c("Control", "High \n\u03b2-Carotene", "High \nLycopene"))) +
  scale_fill_manual(values = c("lightgreen", 
                               "orange",
                               "red"),
                    labels = c("Control",
                               "High \u03b2-Carotene",
                               "High Lycopene"),
                    name = "Treatment") +
   scale_x_discrete(labels = c("b1" = "pre",
                               "b3" = "post")) +
  geom_line(aes(group = subject), colour = "gray", linewidth = 0.15) +
  theme_classic(base_size = 18, base_family = "sans") +
  labs(x = "",
       y = "Log2 relative abundance",
       title = "    
760.5281_6.984 levels at each timepoint") +
   stat_compare_means(method = "t.test", paired = TRUE) +
   theme(legend.position = "none")

It turns out that the feature masking my compound is a c14 isotopologue of 758.5734

df_for_stats %>% 
   filter(mz_rt == "758.5734_6.982") %>%
  ggpaired(x = "period", y = "rel_abund_log2", fill = "treatment", 
           line.color = "gray", 
           line.size = 1, 
           facet.by = "treatment", 
           short.panel.labs = FALSE, panel.labs = list(treatment = c("Control", "High \n\u03b2-Carotene", "High \nLycopene"))) +
  scale_fill_manual(values = c("lightgreen", 
                               "orange",
                               "red"),
                    labels = c("Control",
                               "High \u03b2-Carotene",
                               "High Lycopene"),
                    name = "Treatment") +
   scale_x_discrete(labels = c("b1" = "pre",
                               "b3" = "post")) +
  geom_line(aes(group = subject), colour = "gray", linewidth = 0.15) +
  theme_classic(base_size = 18, base_family = "sans") +
  labs(x = "",
       y = "Log2 relative abundance",
       title = "    
758.5734_6.982 levels at each timepoint") +
   stat_compare_means(method = "t.test", paired = TRUE) +
   theme(legend.position = "none")

Feature annotation

Wrangle

annotated_df <- df_for_stats %>%
  separate(col = mz_rt,
           into = c("mz", "rt"),
           sep = "_") %>%
  select(mz, rt) %>%
  unique()

# annotated_df <- lipidr::add_sample_annotation(annotated_df$mz)

Export

Metabolite df

write_csv(DC_imp_metabind_clust_log2_noQCs, "final-omics-df-lipidomics-pos.csv",col_names = TRUE)